nohup: ignoring input
Please build and install Nvidia apex package with option '--cuda_ext' according to https://github.com/NVIDIA/apex#from-source .
model_base /mnt/data_nas/luyt/VLM_weight/Bunny-v1_0-3B/
Loading Bunny from base model...
load model path directly..... and model_name.lower() qformer_v3_bib_q_instruct_qaprompt_mm_reloadbert_full_0.7719
load vision_tower from pretrained......
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.embeddings.patch_embedding.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.embeddings.patch_embedding.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.embeddings.position_embedding.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.post_layernorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.post_layernorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.probe: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.attention.in_proj_weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.attention.in_proj_bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.attention.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.attention.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.layernorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.layernorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
torch.Size([2560, 1152])
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.embeddings.word_embeddings.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.embeddings.position_embeddings.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.embeddings.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.embeddings.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.attention.self.query.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.attention.self.query.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.attention.self.key.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.attention.self.key.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.attention.self.value.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.attention.self.value.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.attention.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.attention.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.attention.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.attention.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.intermediate.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.intermediate.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.attention.self.query.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.attention.self.query.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.attention.self.key.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.attention.self.key.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.attention.self.value.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.attention.self.value.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.attention.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.attention.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.attention.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.attention.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.intermediate.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.intermediate.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.attention.self.query.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.attention.self.query.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.attention.self.key.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.attention.self.key.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.attention.self.value.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.attention.self.value.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.attention.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.attention.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.attention.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.attention.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.intermediate.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.intermediate.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.attention.self.query.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.attention.self.query.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.attention.self.key.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.attention.self.key.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.attention.self.value.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.attention.self.value.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.attention.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.attention.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.attention.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.attention.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.intermediate.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.intermediate.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.attention.self.query.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.attention.self.query.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.attention.self.key.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.attention.self.key.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.attention.self.value.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.attention.self.value.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.attention.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.attention.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.attention.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.attention.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.intermediate.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.intermediate.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.attention.self.query.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.attention.self.query.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.attention.self.key.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.attention.self.key.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.attention.self.value.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.attention.self.value.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.attention.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.attention.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.attention.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.attention.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.intermediate.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.intermediate.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.attention.self.query.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.attention.self.query.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.attention.self.key.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.attention.self.key.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.attention.self.value.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.attention.self.value.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.attention.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.attention.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.attention.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.attention.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.intermediate.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.intermediate.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.attention.self.query.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.attention.self.query.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.attention.self.key.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.attention.self.key.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.attention.self.value.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.attention.self.value.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.attention.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.attention.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.attention.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.attention.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.intermediate.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.intermediate.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.attention.self.query.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.attention.self.query.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.attention.self.key.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.attention.self.key.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.attention.self.value.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.attention.self.value.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.attention.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.attention.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.attention.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.attention.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.intermediate.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.intermediate.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.attention.self.query.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.attention.self.query.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.attention.self.key.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.attention.self.key.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.attention.self.value.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.attention.self.value.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.attention.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.attention.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.attention.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.attention.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.intermediate.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.intermediate.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.attention.self.query.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.attention.self.query.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.attention.self.key.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.attention.self.key.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.attention.self.value.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.attention.self.value.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.attention.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.attention.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.attention.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.attention.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.intermediate.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.intermediate.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.attention.self.query.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.attention.self.query.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.attention.self.key.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.attention.self.key.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.attention.self.value.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.attention.self.value.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.attention.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.attention.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.attention.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.attention.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.intermediate.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.intermediate.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for cls.predictions.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for cls.predictions.transform.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for cls.predictions.transform.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for cls.predictions.transform.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for cls.predictions.transform.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
Loading pretrained qformer weights...
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.crossattention.self.query.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.crossattention.self.query.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.crossattention.self.key.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.crossattention.self.key.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.crossattention.self.value.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.crossattention.self.value.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.crossattention.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.crossattention.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.crossattention.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.crossattention.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.intermediate_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.intermediate_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.output_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.output_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.output_query.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.0.output_query.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.intermediate_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.intermediate_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.output_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.output_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.output_query.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.1.output_query.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.crossattention.self.query.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.crossattention.self.query.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.crossattention.self.key.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.crossattention.self.key.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.crossattention.self.value.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.crossattention.self.value.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.crossattention.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.crossattention.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.crossattention.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.crossattention.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.intermediate_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.intermediate_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.output_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.output_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.output_query.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.2.output_query.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.intermediate_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.intermediate_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.output_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.output_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.output_query.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.3.output_query.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.crossattention.self.query.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.crossattention.self.query.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.crossattention.self.key.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.crossattention.self.key.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.crossattention.self.value.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.crossattention.self.value.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.crossattention.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.crossattention.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.crossattention.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.crossattention.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.intermediate_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.intermediate_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.output_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.output_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.output_query.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.4.output_query.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.intermediate_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.intermediate_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.output_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.output_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.output_query.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.5.output_query.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.crossattention.self.query.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.crossattention.self.query.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.crossattention.self.key.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.crossattention.self.key.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.crossattention.self.value.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.crossattention.self.value.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.crossattention.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.crossattention.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.crossattention.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.crossattention.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.intermediate_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.intermediate_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.output_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.output_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.output_query.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.6.output_query.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.intermediate_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.intermediate_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.output_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.output_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.output_query.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.7.output_query.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.crossattention.self.query.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.crossattention.self.query.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.crossattention.self.key.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.crossattention.self.key.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.crossattention.self.value.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.crossattention.self.value.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.crossattention.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.crossattention.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.crossattention.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.crossattention.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.intermediate_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.intermediate_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.output_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.output_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.output_query.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.8.output_query.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.intermediate_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.intermediate_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.output_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.output_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.output_query.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.9.output_query.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.crossattention.self.query.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.crossattention.self.query.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.crossattention.self.key.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.crossattention.self.key.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.crossattention.self.value.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.crossattention.self.value.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.crossattention.output.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.crossattention.output.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.crossattention.output.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.crossattention.output.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.intermediate_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.intermediate_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.output_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.output_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.output_query.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.10.output_query.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.intermediate_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.intermediate_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.output_query.dense.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.output_query.dense.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.output_query.LayerNorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bert.encoder.layer.11.output_query.LayerNorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
load vlm_att_encoder from pretrained <All keys matched successfully>
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/pai/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
load vlm_att_ln from pretrained <All keys matched successfully>

Loading checkpoint shards:   0%|                                                                 | 0/2 [00:00<?, ?it/s]
Loading checkpoint shards:  50%|████████████████████████████▌                            | 1/2 [00:03<00:03,  3.66s/it]
Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████| 2/2 [00:04<00:00,  1.92s/it]
Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████| 2/2 [00:04<00:00,  2.18s/it]
BunnyQformer_v3_bib_PhiForCausalLM(
  (model): BunnyQformer_v3_bib_PhiModel(
    (embed_tokens): Embedding(50295, 2560, padding_idx=50256)
    (embed_dropout): Dropout(p=0.0, inplace=False)
    (layers): ModuleList(
      (0-31): 32 x PhiDecoderLayer(
        (self_attn): PhiAttention(
          (q_proj): Linear(in_features=2560, out_features=2560, bias=True)
          (k_proj): Linear(in_features=2560, out_features=2560, bias=True)
          (v_proj): Linear(in_features=2560, out_features=2560, bias=True)
          (dense): Linear(in_features=2560, out_features=2560, bias=True)
          (rotary_emb): PhiRotaryEmbedding()
        )
        (mlp): PhiMLP(
          (activation_fn): NewGELUActivation()
          (fc1): Linear(in_features=2560, out_features=10240, bias=True)
          (fc2): Linear(in_features=10240, out_features=2560, bias=True)
        )
        (input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
        (resid_dropout): Dropout(p=0.1, inplace=False)
      )
    )
    (final_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
    (vision_tower): SigLipVisionTower(
      (vision_tower): SigLipVisionModel(
        (vision_model): SigLipVisionTransformer(
          (embeddings): SigLipVisionEmbeddings(
            (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid)
            (position_embedding): Embedding(729, 1152)
          )
          (encoder): SigLipEncoder(
            (layers): ModuleList(
              (0-25): 26 x SigLipEncoderLayer(
                (self_attn): SigLipAttention(
                  (k_proj): Linear(in_features=1152, out_features=1152, bias=True)
                  (v_proj): Linear(in_features=1152, out_features=1152, bias=True)
                  (q_proj): Linear(in_features=1152, out_features=1152, bias=True)
                  (out_proj): Linear(in_features=1152, out_features=1152, bias=True)
                )
                (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True)
                (mlp): SigLipMLP(
                  (activation_fn): PytorchGELUTanh()
                  (fc1): Linear(in_features=1152, out_features=4304, bias=True)
                  (fc2): Linear(in_features=4304, out_features=1152, bias=True)
                )
                (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True)
              )
            )
          )
          (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True)
          (head): Identity()
        )
      )
    )
    (mm_projector): Sequential(
      (0): Linear(in_features=1152, out_features=2560, bias=True)
      (1): GELU(approximate='none')
      (2): Linear(in_features=2560, out_features=2560, bias=True)
    )
    (vlm_att_ln): LayerNorm((1408,), eps=1e-05, elementwise_affine=True)
    (vlm_att_encoder): BertLMHeadModel(
      (bert): BertModel(
        (embeddings): BertEmbeddings(
          (word_embeddings): Embedding(30523, 768)
          (position_embeddings): Embedding(512, 768)
          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
          (dropout): Dropout(p=0.1, inplace=False)
        )
        (encoder): BertEncoder(
          (layer): ModuleList(
            (0): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (crossattention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=1408, out_features=768, bias=True)
                  (value): Linear(in_features=1408, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (intermediate_query): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output_query): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (1): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (intermediate_query): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output_query): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (2): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (crossattention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=1408, out_features=768, bias=True)
                  (value): Linear(in_features=1408, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (intermediate_query): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output_query): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (3): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (intermediate_query): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output_query): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (4): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (crossattention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=1408, out_features=768, bias=True)
                  (value): Linear(in_features=1408, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (intermediate_query): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output_query): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (5): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (intermediate_query): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output_query): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (6): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (crossattention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=1408, out_features=768, bias=True)
                  (value): Linear(in_features=1408, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (intermediate_query): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output_query): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (7): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (intermediate_query): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output_query): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (8): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (crossattention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=1408, out_features=768, bias=True)
                  (value): Linear(in_features=1408, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (intermediate_query): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output_query): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (9): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (intermediate_query): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output_query): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (10): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (crossattention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=1408, out_features=768, bias=True)
                  (value): Linear(in_features=1408, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (intermediate_query): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output_query): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (11): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (intermediate_query): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output_query): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
          )
        )
      )
      (cls): None
    )
    (vlm_att_projector): Linear(in_features=1152, out_features=1408, bias=True)
    (vlm_att_deprojector): Linear(in_features=768, out_features=1152, bias=True)
    (vlm_cross_attn): vlm_cross_attn(
      (self_attn): MultiheadAttention(
        (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True)
      )
      (linear1): Linear(in_features=2304, out_features=2048, bias=True)
      (dropout): Dropout(p=0.1, inplace=False)
      (linear2): Linear(in_features=2048, out_features=1, bias=True)
      (norm1): LayerNorm((2304,), eps=1e-05, elementwise_affine=True)
      (norm2): LayerNorm((1152,), eps=1e-05, elementwise_affine=True)
      (dropout1): Dropout(p=0.1, inplace=False)
      (dropout2): Dropout(p=0.1, inplace=False)
    )
  )
  (lm_head): Linear(in_features=2560, out_features=50295, bias=False)
)
Loading stage2 weights...
non_lora_trainables.bin of previous stage exits
load additional weight from previous stage: []
Loading LoRA weights from previous stage...
Merging stage2 weights...
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'model.vlm_cross_attn.linear2.bias', 'model.vlm_cross_attn.norm1.weight', 'model.vlm_cross_attn.norm1.bias', 'model.vlm_cross_attn.norm2.weight', 'model.vlm_cross_attn.norm2.bias'])
[]

  0%|                                                                                         | 0/1000 [00:00<?, ?it/s]prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. More blurry
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. More blurry
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.

/home/pai/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:492: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
  warnings.warn(
prompts: [["Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

  0%|                                                                                 | 1/1000 [00:01<26:49,  1.61s/it]
[Running Accuracy]: 0.0000,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 1:   0%| | 1/1000 [00:01<26:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the sharpness of the second image?
A. Similar
B. Clearer
C. More blurry
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the sharpness of the second image?
A. Similar
B. Clearer
C. More blurry
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the sharpness of the second image?\nA. Similar\nB. Clearer\nC. More blurry\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.0000,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 1:   0%| | 2/1000 [00:02<21:1
[Running Accuracy]: 0.0000,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 2:   0%| | 2/1000 [00:02<21:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the sharpness of the second image?\nA. Similar\nB. Clearer\nC. More blurry\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how real is the second image?
A. Similar
B. More real
C. Less real
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how real is the second image?
A. Similar
B. More real
C. Less real
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how real is the second image?\nA. Similar\nB. More real\nC. Less real\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.0000,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 2:   0%| | 3/1000 [00:03<20:5
[Running Accuracy]: 0.0000,[Response]: B.<|endoftext|>, [Correct Ans]: Less real, , [Prog]: 3:   0%| | 3/1000 [00:03<20
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how real is the second image?\nA. Similar\nB. More real\nC. Less real\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image has more severe overexposure?
A. First image
B. Second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image has more severe overexposure?
A. First image
B. Second image
Answer with the option's letter from the given choices directly.

prompts: [["Which image has more severe overexposure?\nA. First image\nB. Second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.0000,[Response]: B.<|endoftext|>, [Correct Ans]: Less real, , [Prog]: 3:   0%| | 4/1000 [00:04<18
[Running Accuracy]: 0.2500,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 4:   0%| | 4/1000 [00:04<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image has more severe overexposure?\nA. First image\nB. Second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the composition of the second image compare to the first image?
A. Better
B. About the same
C. Worse
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the composition of the second image compare to the first image?
A. Better
B. About the same
C. Worse
Answer with the option's letter from the given choices directly.

prompts: [["How does the composition of the second image compare to the first image?\nA. Better\nB. About the same\nC. Worse\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.2500,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 4:   0%| | 5/1000 [00:05<
[Running Accuracy]: 0.2000,[Response]: B.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 5:   0%| | 5/1000 [00:05<16:54
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the composition of the second image compare to the first image?\nA. Better\nB. About the same\nC. Worse\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the sharpness of the second image?
A. Sharper
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the sharpness of the second image?
A. Sharper
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the sharpness of the second image?\nA. Sharper\nB. Blurrier\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.2000,[Response]: B.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 5:   1%| | 6/1000 [00:06<14:22
[Running Accuracy]: 0.1667,[Response]: B.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 6:   1%| | 6/1000 [00:06<14:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the sharpness of the second image?\nA. Sharper\nB. Blurrier\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how would you evaluate the realism of the second image?
A. Less realistic
B. About the same
C. More realistic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how would you evaluate the realism of the second image?
A. Less realistic
B. About the same
C. More realistic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how would you evaluate the realism of the second image?\nA. Less realistic\nB. About the same\nC. More realistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.1667,[Response]: B.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 6:   1%| | 7/1000 [00:06<12:5
[Running Accuracy]: 0.1429,[Response]: C.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 7:   1%| | 7/1000 [00:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how would you evaluate the realism of the second image?\nA. Less realistic\nB. About the same\nC. More realistic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.1429,[Response]: C.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 7:   1%| | 8/1000 [00:
[Running Accuracy]: 0.2500,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 8:   1%| | 8/1000 [00:07<13:20,  
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Sharper
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Sharper
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Sharper\nB. Blurrier\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.2500,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 8:   1%| | 9/1000 [00:08<11:59,  
[Running Accuracy]: 0.2222,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 9:   1%| | 9/1000 [00:08<11:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Sharper\nB. Blurrier\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the authenticity of the second image?
A. more authentic
B. almost the same
C. less authentic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the authenticity of the second image?
A. more authentic
B. almost the same
C. less authentic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the authenticity of the second image?\nA. more authentic\nB. almost the same\nC. less authentic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.2222,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 9:   1%| | 10/1000 [00:08<11
[Running Accuracy]: 0.2000,[Response]: A.<|endoftext|>, [Correct Ans]: less authentic, , [Prog]: 10:   1%| | 10/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the authenticity of the second image?\nA. more authentic\nB. almost the same\nC. less authentic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. More blurry
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. More blurry
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.2000,[Response]: A.<|endoftext|>, [Correct Ans]: less authentic, , [Prog]: 10:   1%| | 11/1000 [0
[Running Accuracy]: 0.1818,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 11:   1%| | 11/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the sharpness of the first image?
A. Clearer
B. About the same
C. Blurrier
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the sharpness of the first image?
A. Clearer
B. About the same
C. Blurrier
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the sharpness of the first image?\nA. Clearer\nB. About the same\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.1818,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 11:   1%| | 12/1000 [0
[Running Accuracy]: 0.1667,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 12:   1%| | 12/1000 [00:09<10
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the sharpness of the first image?\nA. Clearer\nB. About the same\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the illumination of the second image?
A. similar
B. less sufficient
C. more sufficient
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the illumination of the second image?
A. similar
B. less sufficient
C. more sufficient
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the illumination of the second image?\nA. similar\nB. less sufficient\nC. more sufficient\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.1667,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 12:   1%| | 13/1000 [00:10<09
[Running Accuracy]: 0.2308,[Response]: C.<|endoftext|>, [Correct Ans]: more sufficient, , [Prog]: 13:   1%| | 13/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the illumination of the second image?\nA. similar\nB. less sufficient\nC. more sufficient\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Similar
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Similar
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Similar\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.2308,[Response]: C.<|endoftext|>, [Correct Ans]: more sufficient, , [Prog]: 13:   1%| | 14/1000 [
[Running Accuracy]: 0.2857,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 14:   1%| | 14/1000 [00:11<09
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Similar\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image is more severely affected by noise?
A. First image
B. Second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image is more severely affected by noise?
A. First image
B. Second image
Answer with the option's letter from the given choices directly.

prompts: [["Which image is more severely affected by noise?\nA. First image\nB. Second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.2857,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 14:   2%| | 15/1000 [00:11<09
[Running Accuracy]: 0.3333,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 15:   2%| | 15/1000 [00:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image is more severely affected by noise?\nA. First image\nB. Second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are there noise issues in both of these images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are there noise issues in both of these images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are there noise issues in both of these images?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.3333,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 15:   2%| | 16/1000 [00:1
[Running Accuracy]: 0.3750,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 16:   2%| | 16/1000 [00:12<09:33,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are there noise issues in both of these images?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image is affected more by overexposure?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image is affected more by overexposure?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.

prompts: [["Which image is affected more by overexposure?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.3750,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 16:   2%| | 17/1000 [00:13<12:29,
[Running Accuracy]: 0.4118,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 17:   2%| | 17/1000 [00:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image is affected more by overexposure?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is more severely affected by overexposure?
A. the exterior of the first image
B. the ground of the second image
C. the sky of the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is more severely affected by overexposure?
A. the exterior of the first image
B. the ground of the second image
C. the sky of the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is more severely affected by overexposure?\nA. the exterior of the first image\nB. the ground of the second image\nC. the sky of the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.4118,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 17:   2%| | 18/1000 [00:
[Running Accuracy]: 0.3889,[Response]: C.<|endoftext|>, [Correct Ans]: the exterior of the first image, , [Prog]: 18:  
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is more severely affected by overexposure?\nA. the exterior of the first image\nB. the ground of the second image\nC. the sky of the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are there noise issues in both of these two images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are there noise issues in both of these two images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are there noise issues in both of these two images?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A. Yes

[Running Accuracy]: 0.3889,[Response]: C.<|endoftext|>, [Correct Ans]: the exterior of the first image, , [Prog]: 18:  
[Running Accuracy]: 0.4211,[Response]: A. Yes<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 19:   2%| | 19/1000 [00:14<11
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are there noise issues in both of these two images?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A. Yes<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image is more seriously affected by overexposure?
A. First image
B. Second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image is more seriously affected by overexposure?
A. First image
B. Second image
Answer with the option's letter from the given choices directly.

prompts: [["Which image is more seriously affected by overexposure?\nA. First image\nB. Second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.4211,[Response]: A. Yes<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 19:   2%| | 20/1000 [00:15<11
[Running Accuracy]: 0.4500,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 20:   2%| | 20/1000 [00:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image is more seriously affected by overexposure?\nA. First image\nB. Second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image is more severely affected by motion blur?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image is more severely affected by motion blur?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.

prompts: [["Which image is more severely affected by motion blur?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.4500,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 20:   2%| | 21/1000 [00:1
[Running Accuracy]: 0.4762,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 21:   2%| | 21/1000 [00:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image is more severely affected by motion blur?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image has a more severe overexposure?
A. the second image
B. the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image has a more severe overexposure?
A. the second image
B. the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which image has a more severe overexposure?\nA. the second image\nB. the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.4762,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 21:   2%| | 22/1000 [00:
[Running Accuracy]: 0.5000,[Response]: A.<|endoftext|>, [Correct Ans]: the second image, , [Prog]: 22:   2%| | 22/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image has a more severe overexposure?\nA. the second image\nB. the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the texture detail of the first image richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the texture detail of the first image richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the texture detail of the first image richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5000,[Response]: A.<|endoftext|>, [Correct Ans]: the second image, , [Prog]: 22:   2%| | 23/1000 
[Running Accuracy]: 0.5217,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 23:   2%| | 23/1000 [00:17<10:48,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the texture detail of the first image richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images not very realistic?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images not very realistic?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images not very realistic?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5217,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 23:   2%| | 24/1000 [00:17<10:26,
[Running Accuracy]: 0.5000,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 24:   2%| | 24/1000 [00:17<10:26, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images not very realistic?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. Street lamp in the first image
B. Pedestrian in the second image
C. Ground in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. Street lamp in the first image
B. Pedestrian in the second image
C. Ground in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. Street lamp in the first image\nB. Pedestrian in the second image\nC. Ground in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5000,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 24:   2%| | 25/1000 [00:18<09:56, 
[Running Accuracy]: 0.5200,[Response]: A.<|endoftext|>, [Correct Ans]: Street lamp in the first image, , [Prog]: 25:   
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. Street lamp in the first image\nB. Pedestrian in the second image\nC. Ground in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5200,[Response]: A.<|endoftext|>, [Correct Ans]: Street lamp in the first image, , [Prog]: 25:   
[Running Accuracy]: 0.5385,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 26:   3%| | 26/1000 [00:18<09:31, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5385,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 26:   3%| | 27/1000 [00:19<09:14, 
[Running Accuracy]: 0.5556,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 27:   3%| | 27/1000 [00:19<09:14, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.6562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5556,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 27:   3%| | 28/1000 [00:20<09:21, 
[Running Accuracy]: 0.5714,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 28:   3%| | 28/1000 [00:20<09:21, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5714,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 28:   3%| | 29/1000 [00:20<10:38, 
[Running Accuracy]: 0.5517,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 29:   3%| | 29/1000 [00:20<10:38, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image has more sufficient lighting?
A. the first image
B. the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image has more sufficient lighting?
A. the first image
B. the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which image has more sufficient lighting?\nA. the first image\nB. the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5517,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 29:   3%| | 30/1000 [00:21<10:19, 
[Running Accuracy]: 0.5667,[Response]: A.<|endoftext|>, [Correct Ans]: the first image, , [Prog]: 30:   3%| | 30/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image has more sufficient lighting?\nA. the first image\nB. the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image more vivid than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image more vivid than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image more vivid than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5667,[Response]: A.<|endoftext|>, [Correct Ans]: the first image, , [Prog]: 30:   3%| | 31/1000 [
[Running Accuracy]: 0.5806,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 31:   3%| | 31/1000 [00:22<10:13,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image more vivid than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image below is more severely affected by overexposure?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image below is more severely affected by overexposure?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.

prompts: [["Which image below is more severely affected by overexposure?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5806,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 31:   3%| | 32/1000 [00:22<10:08,
[Running Accuracy]: 0.5938,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 32:   3%| | 32/1000 [00:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image below is more severely affected by overexposure?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5938,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 32:   3%| | 33/1000 [00:
[Running Accuracy]: 0.5758,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 33:   3%| | 33/1000 [00:23<09:47,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image below suffers from more severe overexposure?
A. The first image
B. The second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image below suffers from more severe overexposure?
A. The first image
B. The second image
Answer with the option's letter from the given choices directly.

prompts: [["Which image below suffers from more severe overexposure?\nA. The first image\nB. The second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5758,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 33:   3%| | 34/1000 [00:23<09:30,
[Running Accuracy]: 0.5588,[Response]: B.<|endoftext|>, [Correct Ans]: The first image, , [Prog]: 34:   3%| | 34/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image below suffers from more severe overexposure?\nA. The first image\nB. The second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5588,[Response]: B.<|endoftext|>, [Correct Ans]: The first image, , [Prog]: 34:   4%| | 35/1000 [
[Running Accuracy]: 0.5429,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 35:   4%| | 35/1000 [00:24<09:23, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image looks more realistic?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image looks more realistic?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.

prompts: [["Which image looks more realistic?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5429,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 35:   4%| | 36/1000 [00:25<10:08, 
[Running Accuracy]: 0.5556,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 36:   4%| | 36/1000 [00:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image looks more realistic?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the texture detail of the first image richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the texture detail of the first image richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the texture detail of the first image richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5556,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 36:   4%| | 37/1000 [00:
[Running Accuracy]: 0.5405,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 37:   4%| | 37/1000 [00:25<10:07, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the texture detail of the first image richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the texture detail of the first image richer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the texture detail of the first image richer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the texture detail of the first image richer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5405,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 37:   4%| | 38/1000 [00:26<09:43, 
[Running Accuracy]: 0.5263,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 38:   4%| | 38/1000 [00:26<09:43, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the texture detail of the first image richer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image is more severely affected by motion blur?
A. First image
B. Second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image is more severely affected by motion blur?
A. First image
B. Second image
Answer with the option's letter from the given choices directly.

prompts: [["Which image is more severely affected by motion blur?\nA. First image\nB. Second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5263,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 38:   4%| | 39/1000 [00:26<09:36, 
[Running Accuracy]: 0.5128,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 39:   4%| | 39/1000 [00:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image is more severely affected by motion blur?\nA. First image\nB. Second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5128,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 39:   4%| | 40/1000 [00:
[Running Accuracy]: 0.5250,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 40:   4%| | 40/1000 [00:27<09:56,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by overexposure?
A. Background light source of the second image
B. Character in the first image
C. Background of the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by overexposure?
A. Background light source of the second image
B. Character in the first image
C. Background of the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by overexposure?\nA. Background light source of the second image\nB. Character in the first image\nC. Background of the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5250,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 40:   4%| | 41/1000 [00:28<09:48,
[Running Accuracy]: 0.5366,[Response]: A.<|endoftext|>, [Correct Ans]: Background light source of the second image, , [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by overexposure?\nA. Background light source of the second image\nB. Character in the first image\nC. Background of the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5366,[Response]: A.<|endoftext|>, [Correct Ans]: Background light source of the second image, , [
[Running Accuracy]: 0.5238,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 42:   4%| | 42/1000 [00:28<09:31,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5238,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 42:   4%| | 43/1000 [00:29<10:19,
[Running Accuracy]: 0.5116,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 43:   4%| | 43/1000 [00:29<10:19,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5116,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 43:   4%| | 44/1000 [00:30<10:59,
[Running Accuracy]: 0.5000,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 44:   4%| | 44/1000 [00:30<10:59,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how would you rate the authenticity of the first image?
A. About the same
B. Slightly lower
C. Slightly higher
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how would you rate the authenticity of the first image?
A. About the same
B. Slightly lower
C. Slightly higher
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how would you rate the authenticity of the first image?\nA. About the same\nB. Slightly lower\nC. Slightly higher\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5000,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 44:   4%| | 45/1000 [00:31<13:34,
[Running Accuracy]: 0.5111,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 45:   4%| | 45/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how would you rate the authenticity of the first image?\nA. About the same\nB. Slightly lower\nC. Slightly higher\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is there motion blur in these two images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is there motion blur in these two images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is there motion blur in these two images?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5111,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 45:   5%| | 46/1000 [0
[Running Accuracy]: 0.5217,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 46:   5%| | 46/1000 [00:32<12:43, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is there motion blur in these two images?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Have both of these images experienced motion blur?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Have both of these images experienced motion blur?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Have both of these images experienced motion blur?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5217,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 46:   5%| | 47/1000 [00:33<12:52, 
[Running Accuracy]: 0.5106,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 47:   5%| | 47/1000 [00:33<12:52, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Have both of these images experienced motion blur?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which of the following distortions does not appear in the two images?
A. Motion blur
B. Underexposure
C. Overexposure
D. Weak light
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which of the following distortions does not appear in the two images?
A. Motion blur
B. Underexposure
C. Overexposure
D. Weak light
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following distortions does not appear in the two images?\nA. Motion blur\nB. Underexposure\nC. Overexposure\nD. Weak light\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5106,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 47:   5%| | 48/1000 [00:33<12:54, 
[Running Accuracy]: 0.5000,[Response]: B.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 48:   5%| | 48/1000 [00:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which of the following distortions does not appear in the two images?\nA. Motion blur\nB. Underexposure\nC. Overexposure\nD. Weak light\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the second image more realistic than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the second image more realistic than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the second image more realistic than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5000,[Response]: B.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 48:   5%| | 49/1000 [00:3
[Running Accuracy]: 0.5102,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 49:   5%| | 49/1000 [00:34<12:21, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the second image more realistic than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the lighting condition of the first image?
A. slightly worse
B. slightly better
C. similar
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the lighting condition of the first image?
A. slightly worse
B. slightly better
C. similar
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the lighting condition of the first image?\nA. slightly worse\nB. slightly better\nC. similar\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5102,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 49:   5%| | 50/1000 [00:35<13:09, 
[Running Accuracy]: 0.5000,[Response]: B.<|endoftext|>, [Correct Ans]: slightly worse, , [Prog]: 50:   5%| | 50/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the lighting condition of the first image?\nA. slightly worse\nB. slightly better\nC. similar\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first photo, how is the focus of the second photo?
A. Similar
B. Better
C. Worse
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first photo, how is the focus of the second photo?
A. Similar
B. Better
C. Worse
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first photo, how is the focus of the second photo?\nA. Similar\nB. Better\nC. Worse\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5000,[Response]: B.<|endoftext|>, [Correct Ans]: slightly worse, , [Prog]: 50:   5%| | 51/1000 [0
[Running Accuracy]: 0.4902,[Response]: C.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 51:   5%| | 51/1000 [00:36<13:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first photo, how is the focus of the second photo?\nA. Similar\nB. Better\nC. Worse\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image blurrier than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image blurrier than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image blurrier than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.4902,[Response]: C.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 51:   5%| | 52/1000 [00:37<13:
[Running Accuracy]: 0.5000,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 52:   5%| | 52/1000 [00:37<13:12,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image blurrier than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the composition of the first image?
A. Similar
B. More aesthetically pleasing
C. Less aesthetically pleasing
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the composition of the first image?
A. Similar
B. More aesthetically pleasing
C. Less aesthetically pleasing
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the composition of the first image?\nA. Similar\nB. More aesthetically pleasing\nC. Less aesthetically pleasing\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5000,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 52:   5%| | 53/1000 [00:38<13:11,
[Running Accuracy]: 0.4906,[Response]: B.<|endoftext|>, [Correct Ans]: Less aesthetically pleasing, , [Prog]: 53:   5%|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the composition of the first image?\nA. Similar\nB. More aesthetically pleasing\nC. Less aesthetically pleasing\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area in the two images is not affected by blurring?
A. The bushes in the first image
B. The floor in the first image
C. The background in the second image
D. The man in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area in the two images is not affected by blurring?
A. The bushes in the first image
B. The floor in the first image
C. The background in the second image
D. The man in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area in the two images is not affected by blurring?\nA. The bushes in the first image\nB. The floor in the first image\nC. The background in the second image\nD. The man in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.4906,[Response]: B.<|endoftext|>, [Correct Ans]: Less aesthetically pleasing, , [Prog]: 53:   5%|
[Running Accuracy]: 0.5000,[Response]: D.<|endoftext|>, [Correct Ans]: The man in the second image, , [Prog]: 54:   5%|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area in the two images is not affected by blurring?\nA. The bushes in the first image\nB. The floor in the first image\nC. The background in the second image\nD. The man in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the clarity of the first image?
A. Slightly better
B. Slightly worse
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the clarity of the first image?
A. Slightly better
B. Slightly worse
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the clarity of the first image?\nA. Slightly better\nB. Slightly worse\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5000,[Response]: D.<|endoftext|>, [Correct Ans]: The man in the second image, , [Prog]: 54:   6%|
[Running Accuracy]: 0.4909,[Response]: A.<|endoftext|>, [Correct Ans]: Slightly worse, , [Prog]: 55:   6%| | 55/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the clarity of the first image?\nA. Slightly better\nB. Slightly worse\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the composition of the second image better than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the composition of the second image better than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the composition of the second image better than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.6562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.4909,[Response]: A.<|endoftext|>, [Correct Ans]: Slightly worse, , [Prog]: 55:   6%| | 56/1000 [0
[Running Accuracy]: 0.5000,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 56:   6%| | 56/1000 [00:41<15:12,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the composition of the second image better than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part of the two images is more affected by underexposure?
A. The sky in the first image
B. The figure's back in the second image
C. The building in the center of the first image
D. The shop window in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part of the two images is more affected by underexposure?
A. The sky in the first image
B. The figure's back in the second image
C. The building in the center of the first image
D. The shop window in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part of the two images is more affected by underexposure?\nA. The sky in the first image\nB. The figure's back in the second image\nC. The building in the center of the first image\nD. The shop window in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5000,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 56:   6%| | 57/1000 [00:42<15:49,
[Running Accuracy]: 0.5088,[Response]: B.<|endoftext|>, [Correct Ans]: The figure's back in the second image, , [Prog]:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part of the two images is more affected by underexposure?\nA. The sky in the first image\nB. The figure's back in the second image\nC. The building in the center of the first image\nD. The shop window in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the richness of colors in the second image?
A. Similar
B. Less rich in color
C. More rich in color
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the richness of colors in the second image?
A. Similar
B. Less rich in color
C. More rich in color
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the richness of colors in the second image?\nA. Similar\nB. Less rich in color\nC. More rich in color\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5088,[Response]: B.<|endoftext|>, [Correct Ans]: The figure's back in the second image, , [Prog]:
[Running Accuracy]: 0.5172,[Response]: B.<|endoftext|>, [Correct Ans]: Less rich in color, , [Prog]: 58:   6%| | 58/100
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the richness of colors in the second image?\nA. Similar\nB. Less rich in color\nC. More rich in color\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the lighting situation in the first image?
A. Similar
B. Slightly better
C. Slightly worse
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the lighting situation in the first image?
A. Similar
B. Slightly better
C. Slightly worse
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the lighting situation in the first image?\nA. Similar\nB. Slightly better\nC. Slightly worse\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-29.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5172,[Response]: B.<|endoftext|>, [Correct Ans]: Less rich in color, , [Prog]: 58:   6%| | 59/100
[Running Accuracy]: 0.5085,[Response]: C.<|endoftext|>, [Correct Ans]: Slightly better, , [Prog]: 59:   6%| | 59/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the lighting situation in the first image?\nA. Similar\nB. Slightly better\nC. Slightly worse\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What kind of distortion appears in the following two images?
A. Underexposure
B. Overexposure
C. Noise
D. Motion blur
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What kind of distortion appears in the following two images?
A. Underexposure
B. Overexposure
C. Noise
D. Motion blur
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion appears in the following two images?\nA. Underexposure\nB. Overexposure\nC. Noise\nD. Motion blur\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-29.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5085,[Response]: C.<|endoftext|>, [Correct Ans]: Slightly better, , [Prog]: 59:   6%| | 60/1000 [
[Running Accuracy]: 0.5167,[Response]: A.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 60:   6%| | 60/1000 [00
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What kind of distortion appears in the following two images?\nA. Underexposure\nB. Overexposure\nC. Noise\nD. Motion blur\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the second image more realistic than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the second image more realistic than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the second image more realistic than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5167,[Response]: A.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 60:   6%| | 61/1000 [00
[Running Accuracy]: 0.5246,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 61:   6%| | 61/1000 [00:45<13:08, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the second image more realistic than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the second image more realistic than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the second image more realistic than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the second image more realistic than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5246,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 61:   6%| | 62/1000 [00:46<12:54, 
[Running Accuracy]: 0.5323,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 62:   6%| | 62/1000 [00:46<12:54, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the second image more realistic than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the composition of the first image?
A. worse
B. similar
C. better
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the composition of the first image?
A. worse
B. similar
C. better
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the composition of the first image?\nA. worse\nB. similar\nC. better\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5323,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 62:   6%| | 63/1000 [00:47<13:02, 
[Running Accuracy]: 0.5397,[Response]: C.<|endoftext|>, [Correct Ans]: better, , [Prog]: 63:   6%| | 63/1000 [00:47<13:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the composition of the first image?\nA. worse\nB. similar\nC. better\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which of the following distortions did not appear in the two images?
A. Noise
B. Out of focus
C. Overexposure
D. Underexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which of the following distortions did not appear in the two images?
A. Noise
B. Out of focus
C. Overexposure
D. Underexposure
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following distortions did not appear in the two images?\nA. Noise\nB. Out of focus\nC. Overexposure\nD. Underexposure\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.5397,[Response]: C.<|endoftext|>, [Correct Ans]: better, , [Prog]: 63:   6%| | 64/1000 [00:48<13:
[Running Accuracy]: 0.5312,[Response]: D.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 64:   6%| | 64/1000 [00:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which of the following distortions did not appear in the two images?\nA. Noise\nB. Out of focus\nC. Overexposure\nD. Underexposure\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image richer than the color of the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image richer than the color of the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image richer than the color of the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5312,[Response]: D.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 64:   6%| | 65/1000 [00:
[Running Accuracy]: 0.5385,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 65:   6%| | 65/1000 [00:48<13:13,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image richer than the color of the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the lighting situation in the first image?
A. slightly strong
B. almost the same
C. slightly weak
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the lighting situation in the first image?
A. slightly strong
B. almost the same
C. slightly weak
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the lighting situation in the first image?\nA. slightly strong\nB. almost the same\nC. slightly weak\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5385,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 65:   7%| | 66/1000 [00:49<13:03,
[Running Accuracy]: 0.5455,[Response]: C.<|endoftext|>, [Correct Ans]: slightly weak, , [Prog]: 66:   7%| | 66/1000 [00
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the lighting situation in the first image?\nA. slightly strong\nB. almost the same\nC. slightly weak\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which of the following distortions did not appear in the two images?
A. Noise
B. Out of focus
C. Weak lighting
D. Motion blur
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which of the following distortions did not appear in the two images?
A. Noise
B. Out of focus
C. Weak lighting
D. Motion blur
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following distortions did not appear in the two images?\nA. Noise\nB. Out of focus\nC. Weak lighting\nD. Motion blur\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5455,[Response]: C.<|endoftext|>, [Correct Ans]: slightly weak, , [Prog]: 66:   7%| | 67/1000 [00
[Running Accuracy]: 0.5373,[Response]: B.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 67:   7%| | 67/1000 [00:50<13:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which of the following distortions did not appear in the two images?\nA. Noise\nB. Out of focus\nC. Weak lighting\nD. Motion blur\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-29.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5373,[Response]: B.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 67:   7%| | 68/1000 [00:51<13:1
[Running Accuracy]: 0.5441,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 68:   7%| | 68/1000 [00:51<13:18,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the lighting in the first image?
A. Similar
B. Much worse
C. Much stronger
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the lighting in the first image?
A. Similar
B. Much worse
C. Much stronger
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the lighting in the first image?\nA. Similar\nB. Much worse\nC. Much stronger\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5441,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 68:   7%| | 69/1000 [00:52<14:11,
[Running Accuracy]: 0.5507,[Response]: A.<|endoftext|>, [Correct Ans]: Similar, , [Prog]: 69:   7%| | 69/1000 [00:52<14
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the lighting in the first image?\nA. Similar\nB. Much worse\nC. Much stronger\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5507,[Response]: A.<|endoftext|>, [Correct Ans]: Similar, , [Prog]: 69:   7%| | 70/1000 [00:53<13
[Running Accuracy]: 0.5429,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 70:   7%| | 70/1000 [00:53<13:58,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the second image more authentic than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the second image more authentic than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the second image more authentic than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5429,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 70:   7%| | 71/1000 [00:54<13:42,
[Running Accuracy]: 0.5493,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 71:   7%| | 71/1000 [00:54<13:42, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the second image more authentic than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both images rich in color?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both images rich in color?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both images rich in color?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5493,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 71:   7%| | 72/1000 [00:54<13:21, 
[Running Accuracy]: 0.5417,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 72:   7%| | 72/1000 [00:54<13:21, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both images rich in color?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the richness of color in the second image?
A. Almost the same
B. More rich
C. Less rich
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the richness of color in the second image?
A. Almost the same
B. More rich
C. Less rich
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the richness of color in the second image?\nA. Almost the same\nB. More rich\nC. Less rich\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5417,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 72:   7%| | 73/1000 [00:55<12:02, 
[Running Accuracy]: 0.5479,[Response]: B.<|endoftext|>, [Correct Ans]: More rich, , [Prog]: 73:   7%| | 73/1000 [00:55<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the richness of color in the second image?\nA. Almost the same\nB. More rich\nC. Less rich\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both images very rich in color?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both images very rich in color?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both images very rich in color?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5479,[Response]: B.<|endoftext|>, [Correct Ans]: More rich, , [Prog]: 73:   7%| | 74/1000 [00:56<
[Running Accuracy]: 0.5541,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 74:   7%| | 74/1000 [00:56<12:44, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both images very rich in color?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5541,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 74:   8%| | 75/1000 [00:57<12:49, 
[Running Accuracy]: 0.5467,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 75:   8%| | 75/1000 [00:57<12:49,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the focusing of the first image?
A. worse
B. better
C. about the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the focusing of the first image?
A. worse
B. better
C. about the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the focusing of the first image?\nA. worse\nB. better\nC. about the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5467,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 75:   8%| | 76/1000 [00:58<13:08,
[Running Accuracy]: 0.5526,[Response]: A.<|endoftext|>, [Correct Ans]: worse, , [Prog]: 76:   8%| | 76/1000 [00:58<13:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the focusing of the first image?\nA. worse\nB. better\nC. about the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the lighting in the first image?
A. About the same
B. Worse
C. Better
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the lighting in the first image?
A. About the same
B. Worse
C. Better
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the lighting in the first image?\nA. About the same\nB. Worse\nC. Better\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5526,[Response]: A.<|endoftext|>, [Correct Ans]: worse, , [Prog]: 76:   8%| | 77/1000 [00:59<13:1
[Running Accuracy]: 0.5455,[Response]: C.<|endoftext|>, [Correct Ans]: Worse, , [Prog]: 77:   8%| | 77/1000 [00:59<13:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the lighting in the first image?\nA. About the same\nB. Worse\nC. Better\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both images rich in color?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both images rich in color?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both images rich in color?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5455,[Response]: C.<|endoftext|>, [Correct Ans]: Worse, , [Prog]: 77:   8%| | 78/1000 [00:59<11:5
[Running Accuracy]: 0.5513,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 78:   8%| | 78/1000 [00:59<11:54,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both images rich in color?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the first image affected by underexposure?
A. Smaller
B. Bigger
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the first image affected by underexposure?
A. Smaller
B. Bigger
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the first image affected by underexposure?\nA. Smaller\nB. Bigger\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5513,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 78:   8%| | 79/1000 [01:00<11:16,
[Running Accuracy]: 0.5443,[Response]: A.<|endoftext|>, [Correct Ans]: Bigger, , [Prog]: 79:   8%| | 79/1000 [01:00<11:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the first image affected by underexposure?\nA. Smaller\nB. Bigger\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is more affected by blurring?
A. The wall in the second image
B. The girl in the first image
C. The carpet in the second image
D. The light fixture in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is more affected by blurring?
A. The wall in the second image
B. The girl in the first image
C. The carpet in the second image
D. The light fixture in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more affected by blurring?\nA. The wall in the second image\nB. The girl in the first image\nC. The carpet in the second image\nD. The light fixture in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5443,[Response]: A.<|endoftext|>, [Correct Ans]: Bigger, , [Prog]: 79:   8%| | 80/1000 [01:01<11:
[Running Accuracy]: 0.5500,[Response]: B.<|endoftext|>, [Correct Ans]: The girl in the first image, , [Prog]: 80:   8%|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is more affected by blurring?\nA. The wall in the second image\nB. The girl in the first image\nC. The carpet in the second image\nD. The light fixture in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What is the distortion that does not appear in the two images?
A. Overexposure
B. Focus problem
C. Noise
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What is the distortion that does not appear in the two images?
A. Overexposure
B. Focus problem
C. Noise
Answer with the option's letter from the given choices directly.

prompts: [["What is the distortion that does not appear in the two images?\nA. Overexposure\nB. Focus problem\nC. Noise\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5500,[Response]: B.<|endoftext|>, [Correct Ans]: The girl in the first image, , [Prog]: 80:   8%|
[Running Accuracy]: 0.5432,[Response]: B.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 81:   8%| | 81/1000 [01:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What is the distortion that does not appear in the two images?\nA. Overexposure\nB. Focus problem\nC. Noise\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>In which area of the two images is the overexposure more significant?
A. The sky in the first image
B. The building in the first image
C. The bench in the second image
D. The sea surface in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:In which area of the two images is the overexposure more significant?
A. The sky in the first image
B. The building in the first image
C. The bench in the second image
D. The sea surface in the second image
Answer with the option's letter from the given choices directly.

prompts: [["In which area of the two images is the overexposure more significant?\nA. The sky in the first image\nB. The building in the first image\nC. The bench in the second image\nD. The sea surface in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5432,[Response]: B.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 81:   8%| | 82/1000 [01:
[Running Accuracy]: 0.5488,[Response]: A.<|endoftext|>, [Correct Ans]: The sky in the first image, , [Prog]: 82:   8%| 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>In which area of the two images is the overexposure more significant?\nA. The sky in the first image\nB. The building in the first image\nC. The bench in the second image\nD. The sea surface in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which of the following issues is not present in the two images?
A. motion blur
B. overexposure
C. underexposure
D. reflection
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which of the following issues is not present in the two images?
A. motion blur
B. overexposure
C. underexposure
D. reflection
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following issues is not present in the two images?\nA. motion blur\nB. overexposure\nC. underexposure\nD. reflection\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5488,[Response]: A.<|endoftext|>, [Correct Ans]: The sky in the first image, , [Prog]: 82:   8%| 
[Running Accuracy]: 0.5422,[Response]: C.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 83:   8%| | 83/1000 [01:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which of the following issues is not present in the two images?\nA. motion blur\nB. overexposure\nC. underexposure\nD. reflection\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the illumination of the second image brighter?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the illumination of the second image brighter?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the illumination of the second image brighter?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5422,[Response]: C.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 83:   8%| | 84/1000 [01:
[Running Accuracy]: 0.5476,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 84:   8%| | 84/1000 [01:04<12:51,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the illumination of the second image brighter?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What problems do not appear in the two images?
A. Motion blur
B. Overexposure
C. Underexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What problems do not appear in the two images?
A. Motion blur
B. Overexposure
C. Underexposure
Answer with the option's letter from the given choices directly.

prompts: [["What problems do not appear in the two images?\nA. Motion blur\nB. Overexposure\nC. Underexposure\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5476,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 84:   8%| | 85/1000 [01:05<12:45,
[Running Accuracy]: 0.5412,[Response]: C.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 85:   8%| | 85/1000 [01:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What problems do not appear in the two images?\nA. Motion blur\nB. Overexposure\nC. Underexposure\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how would you describe the richness of colors in the first image?
A. Less rich in color
B. About the same
C. More rich in color
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how would you describe the richness of colors in the first image?
A. Less rich in color
B. About the same
C. More rich in color
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how would you describe the richness of colors in the first image?\nA. Less rich in color\nB. About the same\nC. More rich in color\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5412,[Response]: C.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 85:   9%| | 86/1000 [01:0
[Running Accuracy]: 0.5349,[Response]: A.<|endoftext|>, [Correct Ans]: More rich in color, , [Prog]: 86:   9%| | 86/100
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how would you describe the richness of colors in the first image?\nA. Less rich in color\nB. About the same\nC. More rich in color\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What problems are not present in the two images?
A. Low light
B. Blur
C. Motion blur
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What problems are not present in the two images?
A. Low light
B. Blur
C. Motion blur
Answer with the option's letter from the given choices directly.

prompts: [["What problems are not present in the two images?\nA. Low light\nB. Blur\nC. Motion blur\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5349,[Response]: A.<|endoftext|>, [Correct Ans]: More rich in color, , [Prog]: 86:   9%| | 87/100
[Running Accuracy]: 0.5287,[Response]: B.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 87:   9%| | 87/1000 [01:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What problems are not present in the two images?\nA. Low light\nB. Blur\nC. Motion blur\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is more affected by underexposure in the two images?
A. The background in the first image
B. The man in the second image
C. The giraffe in the second image
D. The fruit in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is more affected by underexposure in the two images?
A. The background in the first image
B. The man in the second image
C. The giraffe in the second image
D. The fruit in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more affected by underexposure in the two images?\nA. The background in the first image\nB. The man in the second image\nC. The giraffe in the second image\nD. The fruit in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5287,[Response]: B.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 87:   9%| | 88/1000 [01:0
[Running Accuracy]: 0.5341,[Response]: A.<|endoftext|>, [Correct Ans]: The background in the first image, , [Prog]: 88:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is more affected by underexposure in the two images?\nA. The background in the first image\nB. The man in the second image\nC. The giraffe in the second image\nD. The fruit in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the focusing situation of the first image?
A. Slightly better
B. About the same
C. Slightly worse
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the focusing situation of the first image?
A. Slightly better
B. About the same
C. Slightly worse
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the focusing situation of the first image?\nA. Slightly better\nB. About the same\nC. Slightly worse\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5341,[Response]: A.<|endoftext|>, [Correct Ans]: The background in the first image, , [Prog]: 88:
[Running Accuracy]: 0.5393,[Response]: C.<|endoftext|>, [Correct Ans]: Slightly worse, , [Prog]: 89:   9%| | 89/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the focusing situation of the first image?\nA. Slightly better\nB. About the same\nC. Slightly worse\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5393,[Response]: C.<|endoftext|>, [Correct Ans]: Slightly worse, , [Prog]: 89:   9%| | 90/1000 [0
[Running Accuracy]: 0.5333,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 90:   9%| | 90/1000 [01:09<12:33,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is more affected by underexposure?
A. The brazier in the second image
B. The cotton candy in the second image
C. The living room in the first image
D. The floor in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is more affected by underexposure?
A. The brazier in the second image
B. The cotton candy in the second image
C. The living room in the first image
D. The floor in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more affected by underexposure?\nA. The brazier in the second image\nB. The cotton candy in the second image\nC. The living room in the first image\nD. The floor in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5333,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 90:   9%| | 91/1000 [01:10<12:58,
[Running Accuracy]: 0.5275,[Response]: B.<|endoftext|>, [Correct Ans]: The living room in the first image, , [Prog]: 91
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is more affected by underexposure?\nA. The brazier in the second image\nB. The cotton candy in the second image\nC. The living room in the first image\nD. The floor in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the illumination of the second image stronger than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the illumination of the second image stronger than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the illumination of the second image stronger than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5275,[Response]: B.<|endoftext|>, [Correct Ans]: The living room in the first image, , [Prog]: 91
[Running Accuracy]: 0.5217,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 92:   9%| | 92/1000 [01:11<11:39,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the illumination of the second image stronger than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is more affected by motion blur?
A. The flowers in the first image
B. The statue in the second image
C. The floor in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is more affected by motion blur?
A. The flowers in the first image
B. The statue in the second image
C. The floor in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more affected by motion blur?\nA. The flowers in the first image\nB. The statue in the second image\nC. The floor in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5217,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 92:   9%| | 93/1000 [01:12<13:49,
[Running Accuracy]: 0.5269,[Response]: A.<|endoftext|>, [Correct Ans]: The flowers in the first image, , [Prog]: 93:   
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is more affected by motion blur?\nA. The flowers in the first image\nB. The statue in the second image\nC. The floor in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which problem did not appear in the two images?
A. overexposure
B. motion blur
C. lens flare
D. low light
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which problem did not appear in the two images?
A. overexposure
B. motion blur
C. lens flare
D. low light
Answer with the option's letter from the given choices directly.

prompts: [["Which problem did not appear in the two images?\nA. overexposure\nB. motion blur\nC. lens flare\nD. low light\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.5269,[Response]: A.<|endoftext|>, [Correct Ans]: The flowers in the first image, , [Prog]: 93:   
[Running Accuracy]: 0.5213,[Response]: D.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 94:   9%| | 94/1000 [01:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which problem did not appear in the two images?\nA. overexposure\nB. motion blur\nC. lens flare\nD. low light\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What problem did not appear in the two images?
A. Blurry
B. Low light
C. Overexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What problem did not appear in the two images?
A. Blurry
B. Low light
C. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["What problem did not appear in the two images?\nA. Blurry\nB. Low light\nC. Overexposure\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5213,[Response]: D.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 94:  10%| | 95/1000 [01:1
[Running Accuracy]: 0.5158,[Response]: C.<|endoftext|>, [Correct Ans]: Low light, , [Prog]: 95:  10%| | 95/1000 [01:13<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What problem did not appear in the two images?\nA. Blurry\nB. Low light\nC. Overexposure\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the illumination sufficient for both images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the illumination sufficient for both images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the illumination sufficient for both images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5158,[Response]: C.<|endoftext|>, [Correct Ans]: Low light, , [Prog]: 95:  10%| | 96/1000 [01:14<
[Running Accuracy]: 0.5104,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 96:  10%| | 96/1000 [01:14<12:45, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the illumination sufficient for both images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the lighting of the first image?
A. Much worse
B. About the same
C. Much better
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the lighting of the first image?
A. Much worse
B. About the same
C. Much better
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the lighting of the first image?\nA. Much worse\nB. About the same\nC. Much better\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5104,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 96:  10%| | 97/1000 [01:15<11:36, 
[Running Accuracy]: 0.5052,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 97:  10%| | 97/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the lighting of the first image?\nA. Much worse\nB. About the same\nC. Much better\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Where is the area most affected by motion blur?
A. The stool in the first image
B. The little boy in the second image
C. The background crowd in the second image
D. The flowerpot in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Where is the area most affected by motion blur?
A. The stool in the first image
B. The little boy in the second image
C. The background crowd in the second image
D. The flowerpot in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Where is the area most affected by motion blur?\nA. The stool in the first image\nB. The little boy in the second image\nC. The background crowd in the second image\nD. The flowerpot in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5052,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 97:  10%| | 98/1000 [0
[Running Accuracy]: 0.5000,[Response]: C.<|endoftext|>, [Correct Ans]: The little boy in the second image, , [Prog]: 98
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Where is the area most affected by motion blur?\nA. The stool in the first image\nB. The little boy in the second image\nC. The background crowd in the second image\nD. The flowerpot in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both images very realistic?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both images very realistic?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both images very realistic?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5000,[Response]: C.<|endoftext|>, [Correct Ans]: The little boy in the second image, , [Prog]: 98
[Running Accuracy]: 0.5051,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 99:  10%| | 99/1000 [01:16<11:22, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both images very realistic?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the sharpness of the second image?
A. Much better
B. Much worse
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the sharpness of the second image?
A. Much better
B. Much worse
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the sharpness of the second image?\nA. Much better\nB. Much worse\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5051,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 99:  10%| | 100/1000 [01:17<11:25,
[Running Accuracy]: 0.5000,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 100:  10%| | 100/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the sharpness of the second image?\nA. Much better\nB. Much worse\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the fine texture of the first image?
A. Much clearer
B. About the same
C. Much blurrier
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the fine texture of the first image?
A. Much clearer
B. About the same
C. Much blurrier
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the fine texture of the first image?\nA. Much clearer\nB. About the same\nC. Much blurrier\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5000,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 100:  10%| | 101/1000 
[Running Accuracy]: 0.5050,[Response]: A.<|endoftext|>, [Correct Ans]: Much clearer, , [Prog]: 101:  10%| | 101/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the fine texture of the first image?\nA. Much clearer\nB. About the same\nC. Much blurrier\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the lighting in the first image?
A. Similar
B. Much stronger
C. Much worse
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the lighting in the first image?
A. Similar
B. Much stronger
C. Much worse
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the lighting in the first image?\nA. Similar\nB. Much stronger\nC. Much worse\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5050,[Response]: A.<|endoftext|>, [Correct Ans]: Much clearer, , [Prog]: 101:  10%| | 102/1000 [0
[Running Accuracy]: 0.5000,[Response]: A.<|endoftext|>, [Correct Ans]: Much worse, , [Prog]: 102:  10%| | 102/1000 [01:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the lighting in the first image?\nA. Similar\nB. Much stronger\nC. Much worse\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which distortion is missing in the second image compared to the first image?
A. overexposure
B. low light
C. noise
D. blur
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which distortion is missing in the second image compared to the first image?
A. overexposure
B. low light
C. noise
D. blur
Answer with the option's letter from the given choices directly.

prompts: [["Which distortion is missing in the second image compared to the first image?\nA. overexposure\nB. low light\nC. noise\nD. blur\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5000,[Response]: A.<|endoftext|>, [Correct Ans]: Much worse, , [Prog]: 102:  10%| | 103/1000 [01:
[Running Accuracy]: 0.5049,[Response]: A.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 103:  10%| | 103/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which distortion is missing in the second image compared to the first image?\nA. overexposure\nB. low light\nC. noise\nD. blur\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the composition of the first image more complete compared to the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the composition of the first image more complete compared to the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the composition of the first image more complete compared to the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5049,[Response]: A.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 103:  10%| | 104/1000 [0
[Running Accuracy]: 0.5096,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 104:  10%| | 104/1000 [01:20<10:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the composition of the first image more complete compared to the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What problem is not present in the two images?
A. Underexposure
B. Blur
C. Motion blur
D. Overexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What problem is not present in the two images?
A. Underexposure
B. Blur
C. Motion blur
D. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["What problem is not present in the two images?\nA. Underexposure\nB. Blur\nC. Motion blur\nD. Overexposure\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5096,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 104:  10%| | 105/1000 [01:21<11:0
[Running Accuracy]: 0.5048,[Response]: A.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 105:  10%| | 105/1000 [01
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What problem is not present in the two images?\nA. Underexposure\nB. Blur\nC. Motion blur\nD. Overexposure\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both images very true?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both images very true?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both images very true?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5000], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5048,[Response]: A.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 105:  11%| | 106/1000 [01
[Running Accuracy]: 0.5094,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 106:  11%| | 106/1000 [01:21<10:23
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both images very true?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the colors of the two images both rich?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the colors of the two images both rich?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the colors of the two images both rich?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5094,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 106:  11%| | 107/1000 [01:22<11:03
[Running Accuracy]: 0.5140,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 107:  11%| | 107/1000 [01:22<11:03
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the colors of the two images both rich?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5140,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 107:  11%| | 108/1000 [01:23<10:30
[Running Accuracy]: 0.5185,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 108:  11%| | 108/1000 [01:23<10:30
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, which type of additional distortion is not present in the second image?
A. motion blur
B. overexposure
C. noise
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, which type of additional distortion is not present in the second image?
A. motion blur
B. overexposure
C. noise
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, which type of additional distortion is not present in the second image?\nA. motion blur\nB. overexposure\nC. noise\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5185,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 108:  11%| | 109/1000 [01:23<10:06
[Running Accuracy]: 0.5138,[Response]: A.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 109:  11%| | 109/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, which type of additional distortion is not present in the second image?\nA. motion blur\nB. overexposure\nC. noise\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is affected by motion blur?
A. The mountains in the first image
B. The plants in the first image
C. The girl standing in the second image
D. The girl sitting in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is affected by motion blur?
A. The mountains in the first image
B. The plants in the first image
C. The girl standing in the second image
D. The girl sitting in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is affected by motion blur?\nA. The mountains in the first image\nB. The plants in the first image\nC. The girl standing in the second image\nD. The girl sitting in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5138,[Response]: A.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 109:  11%| | 110/1000 [0
[Running Accuracy]: 0.5182,[Response]: C.<|endoftext|>, [Correct Ans]: The girl standing in the second image, , [Prog]:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is affected by motion blur?\nA. The mountains in the first image\nB. The plants in the first image\nC. The girl standing in the second image\nD. The girl sitting in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how does the first image's authenticity compare?
A. Similar
B. More fake
C. More authentic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how does the first image's authenticity compare?
A. Similar
B. More fake
C. More authentic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how does the first image's authenticity compare?\nA. Similar\nB. More fake\nC. More authentic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5182,[Response]: C.<|endoftext|>, [Correct Ans]: The girl standing in the second image, , [Prog]:
[Running Accuracy]: 0.5135,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 111:  11%| | 111/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how does the first image's authenticity compare?\nA. Similar\nB. More fake\nC. More authentic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What issue is present in the first image that is not present in the second image?
A. noise
B. underexposure
C. motion blur
D. overexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What issue is present in the first image that is not present in the second image?
A. noise
B. underexposure
C. motion blur
D. overexposure
Answer with the option's letter from the given choices directly.

prompts: [["What issue is present in the first image that is not present in the second image?\nA. noise\nB. underexposure\nC. motion blur\nD. overexposure\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5135,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 111:  11%| | 112/1000 
[Running Accuracy]: 0.5089,[Response]: C.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 112:  11%| | 112/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What issue is present in the first image that is not present in the second image?\nA. noise\nB. underexposure\nC. motion blur\nD. overexposure\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What problem is not present in the two images?
A. Overexposure
B. Motion blur
C. Blur
D. Content distortion
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What problem is not present in the two images?
A. Overexposure
B. Motion blur
C. Blur
D. Content distortion
Answer with the option's letter from the given choices directly.

prompts: [["What problem is not present in the two images?\nA. Overexposure\nB. Motion blur\nC. Blur\nD. Content distortion\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5089,[Response]: C.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 112:  11%| | 113/1000 [0
[Running Accuracy]: 0.5044,[Response]: A.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 113:  11%| | 113/1000 [01
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What problem is not present in the two images?\nA. Overexposure\nB. Motion blur\nC. Blur\nD. Content distortion\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the lighting in the second image?
A. Much weaker
B. Much stronger
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the lighting in the second image?
A. Much weaker
B. Much stronger
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the lighting in the second image?\nA. Much weaker\nB. Much stronger\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5044,[Response]: A.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 113:  11%| | 114/1000 [01
[Running Accuracy]: 0.5088,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 114:  11%| | 114/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the lighting in the second image?\nA. Much weaker\nB. Much stronger\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the colors of the two images very bright and vibrant?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the colors of the two images very bright and vibrant?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the colors of the two images very bright and vibrant?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5088,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 114:  12%| | 115/1000 
[Running Accuracy]: 0.5130,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 115:  12%| | 115/1000 [01:27<10:21
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the colors of the two images very bright and vibrant?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both images well-lit?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both images well-lit?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both images well-lit?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5130,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 115:  12%| | 116/1000 [01:28<10:14
[Running Accuracy]: 0.5172,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 116:  12%| | 116/1000 [01:28<10:14
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both images well-lit?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What kind of distortion does the first image not have more than the second image?
A. Halo
B. Focus issue
C. Blur
D. Overexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What kind of distortion does the first image not have more than the second image?
A. Halo
B. Focus issue
C. Blur
D. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion does the first image not have more than the second image?\nA. Halo\nB. Focus issue\nC. Blur\nD. Overexposure\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5000], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5172,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 116:  12%| | 117/1000 [01:28<09:33
[Running Accuracy]: 0.5128,[Response]: B.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 117:  12%| | 117/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What kind of distortion does the first image not have more than the second image?\nA. Halo\nB. Focus issue\nC. Blur\nD. Overexposure\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both compositions of the two images good?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both compositions of the two images good?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both compositions of the two images good?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5128,[Response]: B.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 117:  12%| | 118/1000 [0
[Running Accuracy]: 0.5085,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 118:  12%| | 118/1000 [01:29<10:25
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both compositions of the two images good?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which kind of distortion is not present in the two images?
A. Underexposure
B. Motion blur
C. Overexposure
D. Blur
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which kind of distortion is not present in the two images?
A. Underexposure
B. Motion blur
C. Overexposure
D. Blur
Answer with the option's letter from the given choices directly.

prompts: [["Which kind of distortion is not present in the two images?\nA. Underexposure\nB. Motion blur\nC. Overexposure\nD. Blur\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5085,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 118:  12%| | 119/1000 [01:30<10:53
[Running Accuracy]: 0.5126,[Response]: A.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 119:  12%| | 119/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which kind of distortion is not present in the two images?\nA. Underexposure\nB. Motion blur\nC. Overexposure\nD. Blur\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the clarity of the first image?
A. Slightly better
B. About the same
C. Slightly worse
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the clarity of the first image?
A. Slightly better
B. About the same
C. Slightly worse
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the clarity of the first image?\nA. Slightly better\nB. About the same\nC. Slightly worse\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5126,[Response]: A.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 119:  12%| | 120/1000 [
[Running Accuracy]: 0.5167,[Response]: A.<|endoftext|>, [Correct Ans]: Slightly better, , [Prog]: 120:  12%| | 120/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the clarity of the first image?\nA. Slightly better\nB. About the same\nC. Slightly worse\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the richness of colors in the second image?
A. Similar
B. Richer
C. Poorer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the richness of colors in the second image?
A. Similar
B. Richer
C. Poorer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the richness of colors in the second image?\nA. Similar\nB. Richer\nC. Poorer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5167,[Response]: A.<|endoftext|>, [Correct Ans]: Slightly better, , [Prog]: 120:  12%| | 121/1000
[Running Accuracy]: 0.5207,[Response]: B.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 121:  12%| | 121/1000 [01:31<1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the richness of colors in the second image?\nA. Similar\nB. Richer\nC. Poorer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, what kind of distortion does the first image not have?
A. Underexposed
B. Blurry
C. Motion blur
D. Overexposed
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, what kind of distortion does the first image not have?
A. Underexposed
B. Blurry
C. Motion blur
D. Overexposed
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, what kind of distortion does the first image not have?\nA. Underexposed\nB. Blurry\nC. Motion blur\nD. Overexposed\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.5207,[Response]: B.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 121:  12%| | 122/1000 [01:33<1
[Running Accuracy]: 0.5164,[Response]: D.<|endoftext|>, [Correct Ans]: Underexposed, , [Prog]: 122:  12%| | 122/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, what kind of distortion does the first image not have?\nA. Underexposed\nB. Blurry\nC. Motion blur\nD. Overexposed\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the focus of the first image better than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the focus of the first image better than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the focus of the first image better than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5164,[Response]: D.<|endoftext|>, [Correct Ans]: Underexposed, , [Prog]: 122:  12%| | 123/1000 [0
[Running Accuracy]: 0.5203,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 123:  12%| | 123/1000 [01:33<11:12
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the focus of the first image better than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the focus of the second image?
A. Similar
B. Better
C. Worse
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the focus of the second image?
A. Similar
B. Better
C. Worse
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the focus of the second image?\nA. Similar\nB. Better\nC. Worse\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5203,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 123:  12%| | 124/1000 [01:34<10:24
[Running Accuracy]: 0.5242,[Response]: C.<|endoftext|>, [Correct Ans]: Worse, , [Prog]: 124:  12%| | 124/1000 [01:34<10
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the focus of the second image?\nA. Similar\nB. Better\nC. Worse\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the second image affected by overexposure?
A. Smaller
B. About the same
C. Larger
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the second image affected by overexposure?
A. Smaller
B. About the same
C. Larger
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the second image affected by overexposure?\nA. Smaller\nB. About the same\nC. Larger\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5242,[Response]: C.<|endoftext|>, [Correct Ans]: Worse, , [Prog]: 124:  12%|▏| 125/1000 [01:35<11
[Running Accuracy]: 0.5280,[Response]: C.<|endoftext|>, [Correct Ans]: Larger, , [Prog]: 125:  12%|▏| 125/1000 [01:35<1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the second image affected by overexposure?\nA. Smaller\nB. About the same\nC. Larger\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5280,[Response]: C.<|endoftext|>, [Correct Ans]: Larger, , [Prog]: 125:  13%|▏| 126/1000 [01:35<1
[Running Accuracy]: 0.5238,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 126:  13%|▏| 126/1000 [01:35<11:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the second image more affected by underexposure?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the second image more affected by underexposure?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the second image more affected by underexposure?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5238,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 126:  13%|▏| 127/1000 [01:36<11:2
[Running Accuracy]: 0.5276,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 127:  13%|▏| 127/1000 [01:36<11:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the second image more affected by underexposure?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the second image more authentic than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the second image more authentic than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the second image more authentic than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5276,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 127:  13%|▏| 128/1000 [01:37<10:4
[Running Accuracy]: 0.5312,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 128:  13%|▏| 128/1000 [01:37<10:49
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the second image more authentic than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Was only the first image affected by overexposure?
A. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Was only the first image affected by overexposure?
A. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Was only the first image affected by overexposure?\nA. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5312,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 128:  13%|▏| 129/1000 [01:37<09:56
[Running Accuracy]: 0.5349,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 129:  13%|▏| 129/1000 [01:37<09:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Was only the first image affected by overexposure?\nA. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which issue is not present in the two images?
A. motion blur
B. overexposure
C. blur
D. low pixels
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which issue is not present in the two images?
A. motion blur
B. overexposure
C. blur
D. low pixels
Answer with the option's letter from the given choices directly.

prompts: [["Which issue is not present in the two images?\nA. motion blur\nB. overexposure\nC. blur\nD. low pixels\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5349,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 129:  13%|▏| 130/1000 [01:38<09:2
[Running Accuracy]: 0.5385,[Response]: B.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 130:  13%|▏| 130/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which issue is not present in the two images?\nA. motion blur\nB. overexposure\nC. blur\nD. low pixels\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the second image more blurry?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the second image more blurry?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the second image more blurry?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5385,[Response]: B.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 130:  13%|▏| 131/1000 [0
[Running Accuracy]: 0.5420,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 131:  13%|▏| 131/1000 [01:39<09:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the second image more blurry?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Similar
B. More blurry
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Similar
B. More blurry
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Similar\nB. More blurry\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5420,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 131:  13%|▏| 132/1000 [01:40<11:2
[Running Accuracy]: 0.5455,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 132:  13%|▏| 132/1000 [01
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Similar\nB. More blurry\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the lighting of the second image better than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the lighting of the second image better than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the lighting of the second image better than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5455,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 132:  13%|▏| 133/1000 [01
[Running Accuracy]: 0.5414,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 133:  13%|▏| 133/1000 [01:40<10:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the lighting of the second image better than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the illumination of the second image much stronger than that of the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the illumination of the second image much stronger than that of the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the illumination of the second image much stronger than that of the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5414,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 133:  13%|▏| 134/1000 [01:41<09:4
[Running Accuracy]: 0.5373,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 134:  13%|▏| 134/1000 [01:41<09:48
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the illumination of the second image much stronger than that of the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Does the first image have more overexposure distortion than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Does the first image have more overexposure distortion than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Does the first image have more overexposure distortion than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5373,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 134:  14%|▏| 135/1000 [01:42<10:36
[Running Accuracy]: 0.5407,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 135:  14%|▏| 135/1000 [01:42<10:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Does the first image have more overexposure distortion than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, is the first image more affected by motion blur?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, is the first image more affected by motion blur?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, is the first image more affected by motion blur?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5407,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 135:  14%|▏| 136/1000 [01:43<10:5
[Running Accuracy]: 0.5441,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 136:  14%|▏| 136/1000 [01:43<10:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, is the first image more affected by motion blur?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the lighting condition of the second image compare to the first image?
A. similar
B. much stronger
C. much worse
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the lighting condition of the second image compare to the first image?
A. similar
B. much stronger
C. much worse
Answer with the option's letter from the given choices directly.

prompts: [["How does the lighting condition of the second image compare to the first image?\nA. similar\nB. much stronger\nC. much worse\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5441,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 136:  14%|▏| 137/1000 [01:43<09:5
[Running Accuracy]: 0.5474,[Response]: A.<|endoftext|>, [Correct Ans]: similar, , [Prog]: 137:  14%|▏| 137/1000 [01:43<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the lighting condition of the second image compare to the first image?\nA. similar\nB. much stronger\nC. much worse\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, is the detail texture of the first image clearer?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, is the detail texture of the first image clearer?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, is the detail texture of the first image clearer?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5474,[Response]: A.<|endoftext|>, [Correct Ans]: similar, , [Prog]: 137:  14%|▏| 138/1000 [01:44<
[Running Accuracy]: 0.5435,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 138:  14%|▏| 138/1000 [01:44<09:33
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, is the detail texture of the first image clearer?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the lighting in the first image?
A. Much weaker
B. About the same
C. Much stronger
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the lighting in the first image?
A. Much weaker
B. About the same
C. Much stronger
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the lighting in the first image?\nA. Much weaker\nB. About the same\nC. Much stronger\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5435,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 138:  14%|▏| 139/1000 [01:44<09:28
[Running Accuracy]: 0.5468,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 139:  14%|▏| 139/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the lighting in the first image?\nA. Much weaker\nB. About the same\nC. Much stronger\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the second image affected by overexposure?
A. Similar
B. Slightly smaller
C. Significantly larger
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the second image affected by overexposure?
A. Similar
B. Slightly smaller
C. Significantly larger
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the second image affected by overexposure?\nA. Similar\nB. Slightly smaller\nC. Significantly larger\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5468,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 139:  14%|▏| 140/1000 
[Running Accuracy]: 0.5429,[Response]: A.<|endoftext|>, [Correct Ans]: Significantly larger, , [Prog]: 140:  14%|▏| 140
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the second image affected by overexposure?\nA. Similar\nB. Slightly smaller\nC. Significantly larger\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is more affected by underexposure?
A. Bottom left area of the second image
B. Area on the right bottom of the second image
C. Areas on both sides of the first image
D. Area in the middle of the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is more affected by underexposure?
A. Bottom left area of the second image
B. Area on the right bottom of the second image
C. Areas on both sides of the first image
D. Area in the middle of the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more affected by underexposure?\nA. Bottom left area of the second image\nB. Area on the right bottom of the second image\nC. Areas on both sides of the first image\nD. Area in the middle of the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5429,[Response]: A.<|endoftext|>, [Correct Ans]: Significantly larger, , [Prog]: 140:  14%|▏| 141
[Running Accuracy]: 0.5390,[Response]: A.<|endoftext|>, [Correct Ans]: Area in the middle of the first image, , [Prog]:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is more affected by underexposure?\nA. Bottom left area of the second image\nB. Area on the right bottom of the second image\nC. Areas on both sides of the first image\nD. Area in the middle of the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more affected by motion blur than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more affected by motion blur than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more affected by motion blur than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5390,[Response]: A.<|endoftext|>, [Correct Ans]: Area in the middle of the first image, , [Prog]:
[Running Accuracy]: 0.5423,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 142:  14%|▏| 142/1000 [01:46<08:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more affected by motion blur than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the lighting of the first image stronger than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the lighting of the first image stronger than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the lighting of the first image stronger than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5423,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 142:  14%|▏| 143/1000 [01:47<09:3
[Running Accuracy]: 0.5385,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 143:  14%|▏| 143/1000 [01:47<09:32
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the lighting of the first image stronger than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>The second image is affected by motion blur, is it larger than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:The second image is affected by motion blur, is it larger than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["The second image is affected by motion blur, is it larger than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5385,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 143:  14%|▏| 144/1000 [01:48<10:32
[Running Accuracy]: 0.5417,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 144:  14%|▏| 144/1000 [01:48<10:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>The second image is affected by motion blur, is it larger than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more affected by motion blur compared to the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more affected by motion blur compared to the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more affected by motion blur compared to the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5417,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 144:  14%|▏| 145/1000 [01:48<09:5
[Running Accuracy]: 0.5448,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 145:  14%|▏| 145/1000 [01:48<09:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more affected by motion blur compared to the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the second image more realistic?
A. no
B. yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the second image more realistic?
A. no
B. yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the second image more realistic?\nA. no\nB. yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5448,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 145:  15%|▏| 146/1000 [01:49<09:1
[Running Accuracy]: 0.5479,[Response]: A.<|endoftext|>, [Correct Ans]: no, , [Prog]: 146:  15%|▏| 146/1000 [01:49<09:18
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the second image more realistic?\nA. no\nB. yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the pixel quality of the first image?
A. Much higher
B. About the same
C. Much lower
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the pixel quality of the first image?
A. Much higher
B. About the same
C. Much lower
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the pixel quality of the first image?\nA. Much higher\nB. About the same\nC. Much lower\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5479,[Response]: A.<|endoftext|>, [Correct Ans]: no, , [Prog]: 146:  15%|▏| 147/1000 [01:50<09:12
[Running Accuracy]: 0.5442,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 147:  15%|▏| 147/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the pixel quality of the first image?\nA. Much higher\nB. About the same\nC. Much lower\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area in the two images is more affected by overexposure?
A. The dog in the first image
B. The trees in the second image
C. The floor in the first image
D. The dog in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area in the two images is more affected by overexposure?
A. The dog in the first image
B. The trees in the second image
C. The floor in the first image
D. The dog in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area in the two images is more affected by overexposure?\nA. The dog in the first image\nB. The trees in the second image\nC. The floor in the first image\nD. The dog in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.4844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5442,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 147:  15%|▏| 148/1000 
[Running Accuracy]: 0.5473,[Response]: C.<|endoftext|>, [Correct Ans]: The floor in the first image, , [Prog]: 148:  15
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area in the two images is more affected by overexposure?\nA. The dog in the first image\nB. The trees in the second image\nC. The floor in the first image\nD. The dog in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What distortion is not present in the two images?
A. Halo
B. Noise
C. Motion Blur
D. Underexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What distortion is not present in the two images?
A. Halo
B. Noise
C. Motion Blur
D. Underexposure
Answer with the option's letter from the given choices directly.

prompts: [["What distortion is not present in the two images?\nA. Halo\nB. Noise\nC. Motion Blur\nD. Underexposure\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.5473,[Response]: C.<|endoftext|>, [Correct Ans]: The floor in the first image, , [Prog]: 148:  15
[Running Accuracy]: 0.5436,[Response]: D.<|endoftext|>, [Correct Ans]: Motion Blur, , [Prog]: 149:  15%|▏| 149/1000 [01
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What distortion is not present in the two images?\nA. Halo\nB. Noise\nC. Motion Blur\nD. Underexposure\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5436,[Response]: D.<|endoftext|>, [Correct Ans]: Motion Blur, , [Prog]: 149:  15%|▏| 150/1000 [01
[Running Accuracy]: 0.5467,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 150:  15%|▏| 150/1000 [01:51<09:11
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more truthful than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more truthful than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more truthful than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5467,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 150:  15%|▏| 151/1000 [01:52<08:45
[Running Accuracy]: 0.5430,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 151:  15%|▏| 151/1000 [01:52<08:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more truthful than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What kind of distortion is not present in the two images?
A. Blur
B. Motion blur
C. Underexposure
D. Overexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What kind of distortion is not present in the two images?
A. Blur
B. Motion blur
C. Underexposure
D. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion is not present in the two images?\nA. Blur\nB. Motion blur\nC. Underexposure\nD. Overexposure\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.5430,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 151:  15%|▏| 152/1000 [01:53<08:3
[Running Accuracy]: 0.5461,[Response]: D.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 152:  15%|▏| 152/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What kind of distortion is not present in the two images?\nA. Blur\nB. Motion blur\nC. Underexposure\nD. Overexposure\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>In which area of the two images is more affected by motion blur?
A. The player in the first image
B. The horse in the second image
C. The audience in the background of the first image
D. The background in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:In which area of the two images is more affected by motion blur?
A. The player in the first image
B. The horse in the second image
C. The audience in the background of the first image
D. The background in the second image
Answer with the option's letter from the given choices directly.

prompts: [["In which area of the two images is more affected by motion blur?\nA. The player in the first image\nB. The horse in the second image\nC. The audience in the background of the first image\nD. The background in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5461,[Response]: D.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 152:  15%|▏| 153/1000 [0
[Running Accuracy]: 0.5425,[Response]: B.<|endoftext|>, [Correct Ans]: The background in the second image, , [Prog]: 15
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>In which area of the two images is more affected by motion blur?\nA. The player in the first image\nB. The horse in the second image\nC. The audience in the background of the first image\nD. The background in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the lighting of the second image compare to the first image?
A. worse
B. better
C. similar
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the lighting of the second image compare to the first image?
A. worse
B. better
C. similar
Answer with the option's letter from the given choices directly.

prompts: [["How does the lighting of the second image compare to the first image?\nA. worse\nB. better\nC. similar\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5425,[Response]: B.<|endoftext|>, [Correct Ans]: The background in the second image, , [Prog]: 15
[Running Accuracy]: 0.5455,[Response]: B.<|endoftext|>, [Correct Ans]: better, , [Prog]: 154:  15%|▏| 154/1000 [01:54<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the lighting of the second image compare to the first image?\nA. worse\nB. better\nC. similar\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how real is the first image?
A. More fake
B. More real
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how real is the first image?
A. More fake
B. More real
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how real is the first image?\nA. More fake\nB. More real\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5455,[Response]: B.<|endoftext|>, [Correct Ans]: better, , [Prog]: 154:  16%|▏| 155/1000 [01:54<0
[Running Accuracy]: 0.5419,[Response]: A.<|endoftext|>, [Correct Ans]: More real, , [Prog]: 155:  16%|▏| 155/1000 [01:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how real is the first image?\nA. More fake\nB. More real\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the lighting situation in the first image?
A. Much better
B. About the same
C. Much worse
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the lighting situation in the first image?
A. Much better
B. About the same
C. Much worse
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the lighting situation in the first image?\nA. Much better\nB. About the same\nC. Much worse\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5419,[Response]: A.<|endoftext|>, [Correct Ans]: More real, , [Prog]: 155:  16%|▏| 156/1000 [01:5
[Running Accuracy]: 0.5385,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 156:  16%|▏| 156/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the lighting situation in the first image?\nA. Much better\nB. About the same\nC. Much worse\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, is the proportion of the first image affected by blurring larger?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, is the proportion of the first image affected by blurring larger?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, is the proportion of the first image affected by blurring larger?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5385,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 156:  16%|▏| 157/1000 
[Running Accuracy]: 0.5350,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 157:  16%|▏| 157/1000 [01:56<08:30
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, is the proportion of the first image affected by blurring larger?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which type of distortion does not appear in the two images?
A. Low light
B. Vignetting
C. Noise
D. Motion blur
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which type of distortion does not appear in the two images?
A. Low light
B. Vignetting
C. Noise
D. Motion blur
Answer with the option's letter from the given choices directly.

prompts: [["Which type of distortion does not appear in the two images?\nA. Low light\nB. Vignetting\nC. Noise\nD. Motion blur\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5350,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 157:  16%|▏| 158/1000 [01:56<09:36
[Running Accuracy]: 0.5316,[Response]: A.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 158:  16%|▏| 158/1000 [01:56<09
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which type of distortion does not appear in the two images?\nA. Low light\nB. Vignetting\nC. Noise\nD. Motion blur\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, is the color of the first image more rich and vivid?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, is the color of the first image more rich and vivid?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, is the color of the first image more rich and vivid?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5316,[Response]: A.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 158:  16%|▏| 159/1000 [01:57<10
[Running Accuracy]: 0.5283,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 159:  16%|▏| 159/1000 [01:57<10:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, is the color of the first image more rich and vivid?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the second image more affected by motion blur?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the second image more affected by motion blur?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the second image more affected by motion blur?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5283,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 159:  16%|▏| 160/1000 [01:58<09:4
[Running Accuracy]: 0.5312,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 160:  16%|▏| 160/1000 [01:58<09:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the second image more affected by motion blur?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5312,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 160:  16%|▏| 161/1000 [01:59<09:3
[Running Accuracy]: 0.5342,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 161:  16%|▏| 161/1000 [01:59<09:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What distortion is not present in the two images?
A. overexposure
B. color distortion
C. motion blur
D. low pixel
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What distortion is not present in the two images?
A. overexposure
B. color distortion
C. motion blur
D. low pixel
Answer with the option's letter from the given choices directly.

prompts: [["What distortion is not present in the two images?\nA. overexposure\nB. color distortion\nC. motion blur\nD. low pixel\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5342,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 161:  16%|▏| 162/1000 [01:59<09:1
[Running Accuracy]: 0.5309,[Response]: C.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 162:  16%|▏| 162/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What distortion is not present in the two images?\nA. overexposure\nB. color distortion\nC. motion blur\nD. low pixel\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How is the noise situation in the second image compared to the first image?
A. Much less
B. About the same
C. Much more
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How is the noise situation in the second image compared to the first image?
A. Much less
B. About the same
C. Much more
Answer with the option's letter from the given choices directly.

prompts: [["How is the noise situation in the second image compared to the first image?\nA. Much less\nB. About the same\nC. Much more\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5309,[Response]: C.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 162:  16%|▏| 163/1000 [0
[Running Accuracy]: 0.5337,[Response]: C.<|endoftext|>, [Correct Ans]: Much more, , [Prog]: 163:  16%|▏| 163/1000 [02:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How is the noise situation in the second image compared to the first image?\nA. Much less\nB. About the same\nC. Much more\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the lighting situation in the second image?
A. weaker
B. stronger
C. similar
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the lighting situation in the second image?
A. weaker
B. stronger
C. similar
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the lighting situation in the second image?\nA. weaker\nB. stronger\nC. similar\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5337,[Response]: C.<|endoftext|>, [Correct Ans]: Much more, , [Prog]: 163:  16%|▏| 164/1000 [02:0
[Running Accuracy]: 0.5305,[Response]: A.<|endoftext|>, [Correct Ans]: stronger, , [Prog]: 164:  16%|▏| 164/1000 [02:00
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the lighting situation in the second image?\nA. weaker\nB. stronger\nC. similar\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the second image affected by blurring?
A. Much more severe
B. Slightly more
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the second image affected by blurring?
A. Much more severe
B. Slightly more
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the second image affected by blurring?\nA. Much more severe\nB. Slightly more\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-29.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5305,[Response]: A.<|endoftext|>, [Correct Ans]: stronger, , [Prog]: 164:  16%|▏| 165/1000 [02:01
[Running Accuracy]: 0.5333,[Response]: A.<|endoftext|>, [Correct Ans]: Much more severe, , [Prog]: 165:  16%|▏| 165/100
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the second image affected by blurring?\nA. Much more severe\nB. Slightly more\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the focusing of the second image compare to the first image?
A. Much worse
B. About the same
C. Much better
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the focusing of the second image compare to the first image?
A. Much worse
B. About the same
C. Much better
Answer with the option's letter from the given choices directly.

prompts: [["How does the focusing of the second image compare to the first image?\nA. Much worse\nB. About the same\nC. Much better\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5333,[Response]: A.<|endoftext|>, [Correct Ans]: Much more severe, , [Prog]: 165:  17%|▏| 166/100
[Running Accuracy]: 0.5361,[Response]: C.<|endoftext|>, [Correct Ans]: Much better, , [Prog]: 166:  17%|▏| 166/1000 [02
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the focusing of the second image compare to the first image?\nA. Much worse\nB. About the same\nC. Much better\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the focus of the second image?
A. Similar
B. Stronger
C. Weaker
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the focus of the second image?
A. Similar
B. Stronger
C. Weaker
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the focus of the second image?\nA. Similar\nB. Stronger\nC. Weaker\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5361,[Response]: C.<|endoftext|>, [Correct Ans]: Much better, , [Prog]: 166:  17%|▏| 167/1000 [02
[Running Accuracy]: 0.5389,[Response]: C.<|endoftext|>, [Correct Ans]: Weaker, , [Prog]: 167:  17%|▏| 167/1000 [02:02<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the focus of the second image?\nA. Similar\nB. Stronger\nC. Weaker\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how clear are the texture details of the subject in the second image?
A. About the same
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how clear are the texture details of the subject in the second image?
A. About the same
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how clear are the texture details of the subject in the second image?\nA. About the same\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5389,[Response]: C.<|endoftext|>, [Correct Ans]: Weaker, , [Prog]: 167:  17%|▏| 168/1000 [02:03<0
[Running Accuracy]: 0.5357,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 168:  17%|▏| 168/1000 [02:03<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how clear are the texture details of the subject in the second image?\nA. About the same\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5357,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 168:  17%|▏| 169/1000 [02:03<
[Running Accuracy]: 0.5325,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 169:  17%|▏| 169/1000 [02:03<07:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the richness of colors in the second image?
A. Much poorer
B. Much richer
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the richness of colors in the second image?
A. Much poorer
B. Much richer
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the richness of colors in the second image?\nA. Much poorer\nB. Much richer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5000], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5325,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 169:  17%|▏| 170/1000 [02:04<08:0
[Running Accuracy]: 0.5294,[Response]: B.<|endoftext|>, [Correct Ans]: Much poorer, , [Prog]: 170:  17%|▏| 170/1000 [02
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the richness of colors in the second image?\nA. Much poorer\nB. Much richer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What is the distortion that does not appear in the two images?
A. Motion blur
B. Underexposure
C. Noise
D. Overexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What is the distortion that does not appear in the two images?
A. Motion blur
B. Underexposure
C. Noise
D. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["What is the distortion that does not appear in the two images?\nA. Motion blur\nB. Underexposure\nC. Noise\nD. Overexposure\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.5294,[Response]: B.<|endoftext|>, [Correct Ans]: Much poorer, , [Prog]: 170:  17%|▏| 171/1000 [02
[Running Accuracy]: 0.5263,[Response]: D.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 171:  17%|▏| 171/1000 [02:05<09
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What is the distortion that does not appear in the two images?\nA. Motion blur\nB. Underexposure\nC. Noise\nD. Overexposure\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5263,[Response]: D.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 171:  17%|▏| 172/1000 [02:05<08
[Running Accuracy]: 0.5233,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 172:  17%|▏| 172/1000 [02:05<08:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the lighting condition similar in the two images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the lighting condition similar in the two images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the lighting condition similar in the two images?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5233,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 172:  17%|▏| 173/1000 [02:06<08:2
[Running Accuracy]: 0.5260,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 173:  17%|▏| 173/1000 [02:06<08:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the lighting condition similar in the two images?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is most affected by motion blur?
A. The car in the second image
B. The trees in the second image
C. The sky in the first image
D. The two people in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is most affected by motion blur?
A. The car in the second image
B. The trees in the second image
C. The sky in the first image
D. The two people in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is most affected by motion blur?\nA. The car in the second image\nB. The trees in the second image\nC. The sky in the first image\nD. The two people in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5260,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 173:  17%|▏| 174/1000 [02:06<08:0
[Running Accuracy]: 0.5230,[Response]: A.<|endoftext|>, [Correct Ans]: The trees in the second image, , [Prog]: 174:  1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is most affected by motion blur?\nA. The car in the second image\nB. The trees in the second image\nC. The sky in the first image\nD. The two people in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the focusing of the second image not as good as the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the focusing of the second image not as good as the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the focusing of the second image not as good as the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5230,[Response]: A.<|endoftext|>, [Correct Ans]: The trees in the second image, , [Prog]: 174:  1
[Running Accuracy]: 0.5257,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 175:  18%|▏| 175/1000 [02:07<08:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the focusing of the second image not as good as the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the exposure of the first image not as good as the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the exposure of the first image not as good as the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the exposure of the first image not as good as the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5257,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 175:  18%|▏| 176/1000 [02:07<07:5
[Running Accuracy]: 0.5284,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 176:  18%|▏| 176/1000 [02:07<07:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the exposure of the first image not as good as the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the exposure of the second image much better than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the exposure of the second image much better than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the exposure of the second image much better than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-29.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5284,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 176:  18%|▏| 177/1000 [02:08<08:1
[Running Accuracy]: 0.5311,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 177:  18%|▏| 177/1000 [02:08<08:10
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the exposure of the second image much better than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is most affected by underexposure?
A. Sky in the second image
B. Trees in the second image
C. Sky in the first image
D. Ground in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is most affected by underexposure?
A. Sky in the second image
B. Trees in the second image
C. Sky in the first image
D. Ground in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is most affected by underexposure?\nA. Sky in the second image\nB. Trees in the second image\nC. Sky in the first image\nD. Ground in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5311,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 177:  18%|▏| 178/1000 [02:09<08:15
[Running Accuracy]: 0.5281,[Response]: B.<|endoftext|>, [Correct Ans]: Ground in the first image, , [Prog]: 178:  18%|▏
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is most affected by underexposure?\nA. Sky in the second image\nB. Trees in the second image\nC. Sky in the first image\nD. Ground in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the focus of the first image not as good as the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the focus of the first image not as good as the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the focus of the first image not as good as the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5281,[Response]: B.<|endoftext|>, [Correct Ans]: Ground in the first image, , [Prog]: 178:  18%|▏
[Running Accuracy]: 0.5251,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 179:  18%|▏| 179/1000 [02:09<08:11
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the focus of the first image not as good as the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the lighting condition in the second image?
A. Similar
B. Much better
C. Much worse
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the lighting condition in the second image?
A. Similar
B. Much better
C. Much worse
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the lighting condition in the second image?\nA. Similar\nB. Much better\nC. Much worse\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5251,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 179:  18%|▏| 180/1000 [02:10<08:15
[Running Accuracy]: 0.5222,[Response]: B.<|endoftext|>, [Correct Ans]: Much worse, , [Prog]: 180:  18%|▏| 180/1000 [02:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the lighting condition in the second image?\nA. Similar\nB. Much better\nC. Much worse\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the noise situation in the second image more severe than in the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the noise situation in the second image more severe than in the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the noise situation in the second image more severe than in the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5222,[Response]: B.<|endoftext|>, [Correct Ans]: Much worse, , [Prog]: 180:  18%|▏| 181/1000 [02:
[Running Accuracy]: 0.5249,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 181:  18%|▏| 181/1000 [02:11<08:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the noise situation in the second image more severe than in the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the pixel of the second image much higher than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the pixel of the second image much higher than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the pixel of the second image much higher than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5249,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 181:  18%|▏| 182/1000 [02:11<08:1
[Running Accuracy]: 0.5220,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 182:  18%|▏| 182/1000 [02:11<08:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the pixel of the second image much higher than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What is the distortion that does not appear in the two images?
A. Underexposure
B. Noise
C. Low light
D. Out of focus
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What is the distortion that does not appear in the two images?
A. Underexposure
B. Noise
C. Low light
D. Out of focus
Answer with the option's letter from the given choices directly.

prompts: [["What is the distortion that does not appear in the two images?\nA. Underexposure\nB. Noise\nC. Low light\nD. Out of focus\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5220,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 182:  18%|▏| 183/1000 [02:12<08:2
[Running Accuracy]: 0.5191,[Response]: A.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 183:  18%|▏| 183/1000 [02:12<08
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What is the distortion that does not appear in the two images?\nA. Underexposure\nB. Noise\nC. Low light\nD. Out of focus\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the blur situation in the second image?
A. Much more severe
B. Slightly more
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the blur situation in the second image?
A. Much more severe
B. Slightly more
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the blur situation in the second image?\nA. Much more severe\nB. Slightly more\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5191,[Response]: A.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 183:  18%|▏| 184/1000 [02:13<09
[Running Accuracy]: 0.5217,[Response]: A.<|endoftext|>, [Correct Ans]: Much more severe, , [Prog]: 184:  18%|▏| 184/100
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the blur situation in the second image?\nA. Much more severe\nB. Slightly more\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is most affected by blurring?
A. The bottle in the first image
B. The crowd in the second image
C. The background in the first image
D. The building in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is most affected by blurring?
A. The bottle in the first image
B. The crowd in the second image
C. The background in the first image
D. The building in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is most affected by blurring?\nA. The bottle in the first image\nB. The crowd in the second image\nC. The background in the first image\nD. The building in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5217,[Response]: A.<|endoftext|>, [Correct Ans]: Much more severe, , [Prog]: 184:  18%|▏| 185/100
[Running Accuracy]: 0.5189,[Response]: B.<|endoftext|>, [Correct Ans]: The background in the first image, , [Prog]: 185
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is most affected by blurring?\nA. The bottle in the first image\nB. The crowd in the second image\nC. The background in the first image\nD. The building in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is more severely affected by blurring?
A. In-focus leaf in the second image
B. Wall in the first image
C. Man in the first image
D. Out-of-focus leaf in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is more severely affected by blurring?
A. In-focus leaf in the second image
B. Wall in the first image
C. Man in the first image
D. Out-of-focus leaf in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more severely affected by blurring?\nA. In-focus leaf in the second image\nB. Wall in the first image\nC. Man in the first image\nD. Out-of-focus leaf in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.7188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5189,[Response]: B.<|endoftext|>, [Correct Ans]: The background in the first image, , [Prog]: 185
[Running Accuracy]: 0.5161,[Response]: A.<|endoftext|>, [Correct Ans]: Out-of-focus leaf in the second image, , [Prog]:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is more severely affected by blurring?\nA. In-focus leaf in the second image\nB. Wall in the first image\nC. Man in the first image\nD. Out-of-focus leaf in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both images very real?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both images very real?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both images very real?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5161,[Response]: A.<|endoftext|>, [Correct Ans]: Out-of-focus leaf in the second image, , [Prog]:
[Running Accuracy]: 0.5187,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 187:  19%|▏| 187/1000 [02:15<09:12
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both images very real?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the second image brighter than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the second image brighter than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the second image brighter than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5187,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 187:  19%|▏| 188/1000 [02:15<08:49
[Running Accuracy]: 0.5213,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 188:  19%|▏| 188/1000 [02:15<08:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the second image brighter than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the focusing situation of the second image?
A. Much worse
B. Much better
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the focusing situation of the second image?
A. Much worse
B. Much better
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the focusing situation of the second image?\nA. Much worse\nB. Much better\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5213,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 188:  19%|▏| 189/1000 [02:16<08:2
[Running Accuracy]: 0.5238,[Response]: A.<|endoftext|>, [Correct Ans]: Much worse, , [Prog]: 189:  19%|▏| 189/1000 [02:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the focusing situation of the second image?\nA. Much worse\nB. Much better\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the lighting conditions good for both images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the lighting conditions good for both images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the lighting conditions good for both images?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5238,[Response]: A.<|endoftext|>, [Correct Ans]: Much worse, , [Prog]: 189:  19%|▏| 190/1000 [02:
[Running Accuracy]: 0.5263,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 190:  19%|▏| 190/1000 [02:16<08:08
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the lighting conditions good for both images?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which color looks brighter?
A. The green of the circuit board in the first image
B. The green of the leaves in the second image
C. The grey of the iron table in the first image
D. The color on the cat in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which color looks brighter?
A. The green of the circuit board in the first image
B. The green of the leaves in the second image
C. The grey of the iron table in the first image
D. The color on the cat in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which color looks brighter?\nA. The green of the circuit board in the first image\nB. The green of the leaves in the second image\nC. The grey of the iron table in the first image\nD. The color on the cat in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5263,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 190:  19%|▏| 191/1000 [02:17<07:56
[Running Accuracy]: 0.5288,[Response]: A.<|endoftext|>, [Correct Ans]: The green of the circuit board in the first imag
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which color looks brighter?\nA. The green of the circuit board in the first image\nB. The green of the leaves in the second image\nC. The grey of the iron table in the first image\nD. The color on the cat in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area has the most severe halo and glare?
A. Reflection of the sea in the second image
B. Buildings in the first image
C. Streetlights in the first image
D. Streetlights in the front of the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area has the most severe halo and glare?
A. Reflection of the sea in the second image
B. Buildings in the first image
C. Streetlights in the first image
D. Streetlights in the front of the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area has the most severe halo and glare?\nA. Reflection of the sea in the second image\nB. Buildings in the first image\nC. Streetlights in the first image\nD. Streetlights in the front of the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5288,[Response]: A.<|endoftext|>, [Correct Ans]: The green of the circuit board in the first imag
[Running Accuracy]: 0.5260,[Response]: C.<|endoftext|>, [Correct Ans]: Streetlights in the front of the second image, ,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area has the most severe halo and glare?\nA. Reflection of the sea in the second image\nB. Buildings in the first image\nC. Streetlights in the first image\nD. Streetlights in the front of the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area has clearer details and textures?
A. The soil on the ground in the first image
B. The fur of the bear in the first image
C. The shell of the insect in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area has clearer details and textures?
A. The soil on the ground in the first image
B. The fur of the bear in the first image
C. The shell of the insect in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area has clearer details and textures?\nA. The soil on the ground in the first image\nB. The fur of the bear in the first image\nC. The shell of the insect in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5260,[Response]: C.<|endoftext|>, [Correct Ans]: Streetlights in the front of the second image, ,
[Running Accuracy]: 0.5233,[Response]: B.<|endoftext|>, [Correct Ans]: The shell of the insect in the second image, , [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area has clearer details and textures?\nA. The soil on the ground in the first image\nB. The fur of the bear in the first image\nC. The shell of the insect in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What kind of distortion is not present in the two images?
A. Noise
B. Motion blur
C. Underexposure
D. Overexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What kind of distortion is not present in the two images?
A. Noise
B. Motion blur
C. Underexposure
D. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion is not present in the two images?\nA. Noise\nB. Motion blur\nC. Underexposure\nD. Overexposure\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.5233,[Response]: B.<|endoftext|>, [Correct Ans]: The shell of the insect in the second image, , [
[Running Accuracy]: 0.5206,[Response]: D.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 194:  19%|▏| 194/1000 [02
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What kind of distortion is not present in the two images?\nA. Noise\nB. Motion blur\nC. Underexposure\nD. Overexposure\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the blurriness in the second image?
A. Similar
B. More serious
C. Slighter
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the blurriness in the second image?
A. Similar
B. More serious
C. Slighter
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the blurriness in the second image?\nA. Similar\nB. More serious\nC. Slighter\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5206,[Response]: D.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 194:  20%|▏| 195/1000 [02
[Running Accuracy]: 0.5231,[Response]: B.<|endoftext|>, [Correct Ans]: More serious, , [Prog]: 195:  20%|▏| 195/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the blurriness in the second image?\nA. Similar\nB. More serious\nC. Slighter\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more blurry?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more blurry?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more blurry?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5231,[Response]: B.<|endoftext|>, [Correct Ans]: More serious, , [Prog]: 195:  20%|▏| 196/1000 [0
[Running Accuracy]: 0.5255,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 196:  20%|▏| 196/1000 [02:20<07:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more blurry?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What is the issue that does not appear in the two images?
A. overexposure
B. don't know
C. out of focus
D. motion blur
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What is the issue that does not appear in the two images?
A. overexposure
B. don't know
C. out of focus
D. motion blur
Answer with the option's letter from the given choices directly.

prompts: [["What is the issue that does not appear in the two images?\nA. overexposure\nB. don't know\nC. out of focus\nD. motion blur\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5255,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 196:  20%|▏| 197/1000 [02:20<07:2
[Running Accuracy]: 0.5228,[Response]: A.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 197:  20%|▏| 197/1000 [02
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What is the issue that does not appear in the two images?\nA. overexposure\nB. don't know\nC. out of focus\nD. motion blur\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the blurriness of the second image?
A. Similar
B. Much more severe
C. Slightly more severe
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the blurriness of the second image?
A. Similar
B. Much more severe
C. Slightly more severe
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the blurriness of the second image?\nA. Similar\nB. Much more severe\nC. Slightly more severe\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5228,[Response]: A.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 197:  20%|▏| 198/1000 [02
[Running Accuracy]: 0.5253,[Response]: B.<|endoftext|>, [Correct Ans]: Much more severe, , [Prog]: 198:  20%|▏| 198/100
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the blurriness of the second image?\nA. Similar\nB. Much more severe\nC. Slightly more severe\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is more affected by blurring?
A. The crowd in the second image
B. The cake in the first image
C. The bananas in the second image
D. The wine glass in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is more affected by blurring?
A. The crowd in the second image
B. The cake in the first image
C. The bananas in the second image
D. The wine glass in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more affected by blurring?\nA. The crowd in the second image\nB. The cake in the first image\nC. The bananas in the second image\nD. The wine glass in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5253,[Response]: B.<|endoftext|>, [Correct Ans]: Much more severe, , [Prog]: 198:  20%|▏| 199/100
[Running Accuracy]: 0.5226,[Response]: B.<|endoftext|>, [Correct Ans]: The wine glass in the first image, , [Prog]: 199
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is more affected by blurring?\nA. The crowd in the second image\nB. The cake in the first image\nC. The bananas in the second image\nD. The wine glass in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Lower
B. Higher
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Lower
B. Higher
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Lower\nB. Higher\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5226,[Response]: B.<|endoftext|>, [Correct Ans]: The wine glass in the first image, , [Prog]: 199
[Running Accuracy]: 0.5200,[Response]: B.<|endoftext|>, [Correct Ans]: Lower, , [Prog]: 200:  20%|▏| 200/1000 [02:22<07
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Lower\nB. Higher\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, is the distortion of the lines more severe in the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, is the distortion of the lines more severe in the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, is the distortion of the lines more severe in the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5200,[Response]: B.<|endoftext|>, [Correct Ans]: Lower, , [Prog]: 200:  20%|▏| 201/1000 [02:23<07
[Running Accuracy]: 0.5174,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 201:  20%|▏| 201/1000 [02:23<07:41
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, is the distortion of the lines more severe in the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is more severely affected by overexposure?
A. Upper half of the second image
B. Lower half of the second image
C. Dumplings behind the lens in the first image
D. Soup in front of the lens in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is more severely affected by overexposure?
A. Upper half of the second image
B. Lower half of the second image
C. Dumplings behind the lens in the first image
D. Soup in front of the lens in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more severely affected by overexposure?\nA. Upper half of the second image\nB. Lower half of the second image\nC. Dumplings behind the lens in the first image\nD. Soup in front of the lens in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5174,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 201:  20%|▏| 202/1000 [02:24<08:16
[Running Accuracy]: 0.5198,[Response]: A.<|endoftext|>, [Correct Ans]: Upper half of the second image, , [Prog]: 202:  
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is more severely affected by overexposure?\nA. Upper half of the second image\nB. Lower half of the second image\nC. Dumplings behind the lens in the first image\nD. Soup in front of the lens in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the sharpness of the second image?
A. much higher
B. much lower
C. about the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the sharpness of the second image?
A. much higher
B. much lower
C. about the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the sharpness of the second image?\nA. much higher\nB. much lower\nC. about the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5198,[Response]: A.<|endoftext|>, [Correct Ans]: Upper half of the second image, , [Prog]: 202:  
[Running Accuracy]: 0.5172,[Response]: B.<|endoftext|>, [Correct Ans]: much higher, , [Prog]: 203:  20%|▏| 203/1000 [02
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the sharpness of the second image?\nA. much higher\nB. much lower\nC. about the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the focus of the first image much worse than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the focus of the first image much worse than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the focus of the first image much worse than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5172,[Response]: B.<|endoftext|>, [Correct Ans]: much higher, , [Prog]: 203:  20%|▏| 204/1000 [02
[Running Accuracy]: 0.5196,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 204:  20%|▏| 204/1000 [02:25<07:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the focus of the first image much worse than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is more affected by low light?
A. The man in front of the lens in the first picture
B. The bus in the first picture
C. The fish in the second picture
D. The leaves in the background of the second picture
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is more affected by low light?
A. The man in front of the lens in the first picture
B. The bus in the first picture
C. The fish in the second picture
D. The leaves in the background of the second picture
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more affected by low light?\nA. The man in front of the lens in the first picture\nB. The bus in the first picture\nC. The fish in the second picture\nD. The leaves in the background of the second picture\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5196,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 204:  20%|▏| 205/1000 [02:25<07:2
[Running Accuracy]: 0.5171,[Response]: A.<|endoftext|>, [Correct Ans]: The leaves in the background of the second pictu
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is more affected by low light?\nA. The man in front of the lens in the first picture\nB. The bus in the first picture\nC. The fish in the second picture\nD. The leaves in the background of the second picture\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is more affected by motion blur?
A. The man in the second image
B. The man's back in the first image
C. The man and table in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is more affected by motion blur?
A. The man in the second image
B. The man's back in the first image
C. The man and table in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more affected by motion blur?\nA. The man in the second image\nB. The man's back in the first image\nC. The man and table in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5171,[Response]: A.<|endoftext|>, [Correct Ans]: The leaves in the background of the second pictu
[Running Accuracy]: 0.5146,[Response]: A.<|endoftext|>, [Correct Ans]: The man's back in the first image, , [Prog]: 206
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is more affected by motion blur?\nA. The man in the second image\nB. The man's back in the first image\nC. The man and table in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the noise situation in the second image compare to the first image?
A. Much more severe
B. Similar
C. Much slighter
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the noise situation in the second image compare to the first image?
A. Much more severe
B. Similar
C. Much slighter
Answer with the option's letter from the given choices directly.

prompts: [["How does the noise situation in the second image compare to the first image?\nA. Much more severe\nB. Similar\nC. Much slighter\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5146,[Response]: A.<|endoftext|>, [Correct Ans]: The man's back in the first image, , [Prog]: 206
[Running Accuracy]: 0.5121,[Response]: A.<|endoftext|>, [Correct Ans]: Much slighter, , [Prog]: 207:  21%|▏| 207/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the noise situation in the second image compare to the first image?\nA. Much more severe\nB. Similar\nC. Much slighter\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the impact of motion blur on the second image much more severe than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the impact of motion blur on the second image much more severe than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the impact of motion blur on the second image much more severe than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5121,[Response]: A.<|endoftext|>, [Correct Ans]: Much slighter, , [Prog]: 207:  21%|▏| 208/1000 [
[Running Accuracy]: 0.5144,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 208:  21%|▏| 208/1000 [02:27<07:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the impact of motion blur on the second image much more severe than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the second image sharper than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the second image sharper than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the second image sharper than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5000], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-29.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5144,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 208:  21%|▏| 209/1000 [02:27<07:1
[Running Accuracy]: 0.5167,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 209:  21%|▏| 209/1000 [02:27<07:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the second image sharper than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the lighting in the second image?
A. Similar
B. Stronger
C. Weaker
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the lighting in the second image?
A. Similar
B. Stronger
C. Weaker
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the lighting in the second image?\nA. Similar\nB. Stronger\nC. Weaker\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5167,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 209:  21%|▏| 210/1000 [02:28<07:1
[Running Accuracy]: 0.5143,[Response]: A.<|endoftext|>, [Correct Ans]: Stronger, , [Prog]: 210:  21%|▏| 210/1000 [02:28
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the lighting in the second image?\nA. Similar\nB. Stronger\nC. Weaker\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of texture details in the second image?
A. About the same
B. Much clearer
C. Much blurrier
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of texture details in the second image?
A. About the same
B. Much clearer
C. Much blurrier
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of texture details in the second image?\nA. About the same\nB. Much clearer\nC. Much blurrier\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5143,[Response]: A.<|endoftext|>, [Correct Ans]: Stronger, , [Prog]: 210:  21%|▏| 211/1000 [02:28
[Running Accuracy]: 0.5118,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 211:  21%|▏| 211/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of texture details in the second image?\nA. About the same\nB. Much clearer\nC. Much blurrier\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is more severely affected by blurring?
A. The dining table area in the second image
B. The two girls in the second image
C. The clock tower in the first image
D. The forest in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is more severely affected by blurring?
A. The dining table area in the second image
B. The two girls in the second image
C. The clock tower in the first image
D. The forest in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more severely affected by blurring?\nA. The dining table area in the second image\nB. The two girls in the second image\nC. The clock tower in the first image\nD. The forest in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5118,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 211:  21%|▏| 212/1000 
[Running Accuracy]: 0.5094,[Response]: B.<|endoftext|>, [Correct Ans]: The dining table area in the second image, , [Pr
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is more severely affected by blurring?\nA. The dining table area in the second image\nB. The two girls in the second image\nC. The clock tower in the first image\nD. The forest in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the clarity of the first image?
A. Much clearer
B. Similar
C. Much blurrier
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the clarity of the first image?
A. Much clearer
B. Similar
C. Much blurrier
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the clarity of the first image?\nA. Much clearer\nB. Similar\nC. Much blurrier\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5094,[Response]: B.<|endoftext|>, [Correct Ans]: The dining table area in the second image, , [Pr
[Running Accuracy]: 0.5070,[Response]: C.<|endoftext|>, [Correct Ans]: Much clearer, , [Prog]: 213:  21%|▏| 213/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the clarity of the first image?\nA. Much clearer\nB. Similar\nC. Much blurrier\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is more affected by overexposure?
A. Banana in picture one
B. Grass in picture two
C. Sky in picture two
D. White cup in the bottom left of picture one
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is more affected by overexposure?
A. Banana in picture one
B. Grass in picture two
C. Sky in picture two
D. White cup in the bottom left of picture one
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more affected by overexposure?\nA. Banana in picture one\nB. Grass in picture two\nC. Sky in picture two\nD. White cup in the bottom left of picture one\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5070,[Response]: C.<|endoftext|>, [Correct Ans]: Much clearer, , [Prog]: 213:  21%|▏| 214/1000 [0
[Running Accuracy]: 0.5047,[Response]: C.<|endoftext|>, [Correct Ans]: White cup in the bottom left of picture one, , [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is more affected by overexposure?\nA. Banana in picture one\nB. Grass in picture two\nC. Sky in picture two\nD. White cup in the bottom left of picture one\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the impact of weak light on the second image greater than that on the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the impact of weak light on the second image greater than that on the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the impact of weak light on the second image greater than that on the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5047,[Response]: C.<|endoftext|>, [Correct Ans]: White cup in the bottom left of picture one, , [
[Running Accuracy]: 0.5070,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 215:  22%|▏| 215/1000 [02:31<07:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the impact of weak light on the second image greater than that on the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the noise situation in the first image?
A. Much more severe
B. Slightly more
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the noise situation in the first image?
A. Much more severe
B. Slightly more
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the noise situation in the first image?\nA. Much more severe\nB. Slightly more\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5070,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 215:  22%|▏| 216/1000 [02:31<07:3
[Running Accuracy]: 0.5093,[Response]: A.<|endoftext|>, [Correct Ans]: Much more severe, , [Prog]: 216:  22%|▏| 216/100
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the noise situation in the first image?\nA. Much more severe\nB. Slightly more\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is more affected by motion blur?
A. Ground in the first image
B. Car in the first image
C. Plane in the second image
D. Background in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is more affected by motion blur?
A. Ground in the first image
B. Car in the first image
C. Plane in the second image
D. Background in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more affected by motion blur?\nA. Ground in the first image\nB. Car in the first image\nC. Plane in the second image\nD. Background in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5093,[Response]: A.<|endoftext|>, [Correct Ans]: Much more severe, , [Prog]: 216:  22%|▏| 217/100
[Running Accuracy]: 0.5069,[Response]: A.<|endoftext|>, [Correct Ans]: Background in the second image, , [Prog]: 217:  
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is more affected by motion blur?\nA. Ground in the first image\nB. Car in the first image\nC. Plane in the second image\nD. Background in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5069,[Response]: A.<|endoftext|>, [Correct Ans]: Background in the second image, , [Prog]: 217:  
[Running Accuracy]: 0.5046,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 218:  22%|▏| 218/1000 [02:32<07:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which type of distortion is more severe in the second image compared to the first image?
A. Underexposure
B. Low light
C. Out of focus
D. Noise
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which type of distortion is more severe in the second image compared to the first image?
A. Underexposure
B. Low light
C. Out of focus
D. Noise
Answer with the option's letter from the given choices directly.

prompts: [["Which type of distortion is more severe in the second image compared to the first image?\nA. Underexposure\nB. Low light\nC. Out of focus\nD. Noise\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5046,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 218:  22%|▏| 219/1000 [02:33<08:1
[Running Accuracy]: 0.5068,[Response]: C.<|endoftext|>, [Correct Ans]: Out of focus, , [Prog]: 219:  22%|▏| 219/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which type of distortion is more severe in the second image compared to the first image?\nA. Underexposure\nB. Low light\nC. Out of focus\nD. Noise\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image has more severe distortion, the first image or the second image?
A. Motion blur
B. Blur
C. Noise
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image has more severe distortion, the first image or the second image?
A. Motion blur
B. Blur
C. Noise
Answer with the option's letter from the given choices directly.

prompts: [["Which image has more severe distortion, the first image or the second image?\nA. Motion blur\nB. Blur\nC. Noise\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5068,[Response]: C.<|endoftext|>, [Correct Ans]: Out of focus, , [Prog]: 219:  22%|▏| 220/1000 [0
[Running Accuracy]: 0.5045,[Response]: A.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 220:  22%|▏| 220/1000 [02:34<07
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image has more severe distortion, the first image or the second image?\nA. Motion blur\nB. Blur\nC. Noise\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What is a more serious problem in the first image compared to the second image?
A. halo
B. underexposure
C. overexposure
D. unrealistic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What is a more serious problem in the first image compared to the second image?
A. halo
B. underexposure
C. overexposure
D. unrealistic
Answer with the option's letter from the given choices directly.

prompts: [["What is a more serious problem in the first image compared to the second image?\nA. halo\nB. underexposure\nC. overexposure\nD. unrealistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5045,[Response]: A.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 220:  22%|▏| 221/1000 [02:34<07
[Running Accuracy]: 0.5023,[Response]: A.<|endoftext|>, [Correct Ans]: underexposure, , [Prog]: 221:  22%|▏| 221/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What is a more serious problem in the first image compared to the second image?\nA. halo\nB. underexposure\nC. overexposure\nD. unrealistic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>In the problem of which is more severe between the first image and the second image, which of the following is not present?
A. Snowflake
B. Strong light
C. Low light
D. Overexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:In the problem of which is more severe between the first image and the second image, which of the following is not present?
A. Snowflake
B. Strong light
C. Low light
D. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["In the problem of which is more severe between the first image and the second image, which of the following is not present?\nA. Snowflake\nB. Strong light\nC. Low light\nD. Overexposure\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5023,[Response]: A.<|endoftext|>, [Correct Ans]: underexposure, , [Prog]: 221:  22%|▏| 222/1000 [
[Running Accuracy]: 0.5045,[Response]: C.<|endoftext|>, [Correct Ans]: Low light, , [Prog]: 222:  22%|▏| 222/1000 [02:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>In the problem of which is more severe between the first image and the second image, which of the following is not present?\nA. Snowflake\nB. Strong light\nC. Low light\nD. Overexposure\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how much is the second image affected by motion blur?
A. Slightly more
B. More severe
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how much is the second image affected by motion blur?
A. Slightly more
B. More severe
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how much is the second image affected by motion blur?\nA. Slightly more\nB. More severe\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5045,[Response]: C.<|endoftext|>, [Correct Ans]: Low light, , [Prog]: 222:  22%|▏| 223/1000 [02:3
[Running Accuracy]: 0.5067,[Response]: B.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 223:  22%|▏| 223/1000 [02
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how much is the second image affected by motion blur?\nA. Slightly more\nB. More severe\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is more severely affected by overexposure?
A. The trees in the first image
B. The kitten in the second image
C. The rider in the first image
D. The grassland in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is more severely affected by overexposure?
A. The trees in the first image
B. The kitten in the second image
C. The rider in the first image
D. The grassland in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more severely affected by overexposure?\nA. The trees in the first image\nB. The kitten in the second image\nC. The rider in the first image\nD. The grassland in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5067,[Response]: B.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 223:  22%|▏| 224/1000 [02
[Running Accuracy]: 0.5045,[Response]: A.<|endoftext|>, [Correct Ans]: The grassland in the second image, , [Prog]: 224
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is more severely affected by overexposure?\nA. The trees in the first image\nB. The kitten in the second image\nC. The rider in the first image\nD. The grassland in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the first image affected by blurring?
A. Similar
B. Slightly more
C. Much more severe
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the first image affected by blurring?
A. Similar
B. Slightly more
C. Much more severe
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the first image affected by blurring?\nA. Similar\nB. Slightly more\nC. Much more severe\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5045,[Response]: A.<|endoftext|>, [Correct Ans]: The grassland in the second image, , [Prog]: 224
[Running Accuracy]: 0.5067,[Response]: C.<|endoftext|>, [Correct Ans]: Much more severe, , [Prog]: 225:  22%|▏| 225/100
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the first image affected by blurring?\nA. Similar\nB. Slightly more\nC. Much more severe\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What is the more severe distortion in the first image compared to the second image?
A. Noise
B. Overexposure
C. Low Light
D. Underexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What is the more severe distortion in the first image compared to the second image?
A. Noise
B. Overexposure
C. Low Light
D. Underexposure
Answer with the option's letter from the given choices directly.

prompts: [["What is the more severe distortion in the first image compared to the second image?\nA. Noise\nB. Overexposure\nC. Low Light\nD. Underexposure\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.5067,[Response]: C.<|endoftext|>, [Correct Ans]: Much more severe, , [Prog]: 225:  23%|▏| 226/100
[Running Accuracy]: 0.5044,[Response]: D.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 226:  23%|▏| 226/1000 [02:38<08
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What is the more severe distortion in the first image compared to the second image?\nA. Noise\nB. Overexposure\nC. Low Light\nD. Underexposure\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Among the problems in which picture one is more serious than picture two, which of the following is not included?
A. Snowflake
B. Over-sharpening
C. Overexposure
D. Focus
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Among the problems in which picture one is more serious than picture two, which of the following is not included?
A. Snowflake
B. Over-sharpening
C. Overexposure
D. Focus
Answer with the option's letter from the given choices directly.

prompts: [["Among the problems in which picture one is more serious than picture two, which of the following is not included?\nA. Snowflake\nB. Over-sharpening\nC. Overexposure\nD. Focus\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.5044,[Response]: D.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 226:  23%|▏| 227/1000 [02:38<07
[Running Accuracy]: 0.5066,[Response]: D.<|endoftext|>, [Correct Ans]: Focus, , [Prog]: 227:  23%|▏| 227/1000 [02:38<07
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Among the problems in which picture one is more serious than picture two, which of the following is not included?\nA. Snowflake\nB. Over-sharpening\nC. Overexposure\nD. Focus\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the noise in the first image significantly more severe than in the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the noise in the first image significantly more severe than in the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the noise in the first image significantly more severe than in the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5066,[Response]: D.<|endoftext|>, [Correct Ans]: Focus, , [Prog]: 227:  23%|▏| 228/1000 [02:39<07
[Running Accuracy]: 0.5044,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 228:  23%|▏| 228/1000 [02:39<07:36
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the noise in the first image significantly more severe than in the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How is the focusing of the second image compared to the first image?
A. Much worse
B. About the same
C. Much better
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How is the focusing of the second image compared to the first image?
A. Much worse
B. About the same
C. Much better
Answer with the option's letter from the given choices directly.

prompts: [["How is the focusing of the second image compared to the first image?\nA. Much worse\nB. About the same\nC. Much better\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5044,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 228:  23%|▏| 229/1000 [02:39<07:23
[Running Accuracy]: 0.5066,[Response]: A.<|endoftext|>, [Correct Ans]: Much worse, , [Prog]: 229:  23%|▏| 229/1000 [02:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How is the focusing of the second image compared to the first image?\nA. Much worse\nB. About the same\nC. Much better\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which of the following is NOT a more serious problem in the first image compared to the second image?
A. Blur
B. Overexposure
C. Motion blur
D. Noise
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which of the following is NOT a more serious problem in the first image compared to the second image?
A. Blur
B. Overexposure
C. Motion blur
D. Noise
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following is NOT a more serious problem in the first image compared to the second image?\nA. Blur\nB. Overexposure\nC. Motion blur\nD. Noise\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5066,[Response]: A.<|endoftext|>, [Correct Ans]: Much worse, , [Prog]: 229:  23%|▏| 230/1000 [02:
[Running Accuracy]: 0.5043,[Response]: B.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 230:  23%|▏| 230/1000 [02:40<07
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which of the following is NOT a more serious problem in the first image compared to the second image?\nA. Blur\nB. Overexposure\nC. Motion blur\nD. Noise\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is more affected by blur?
A. Yellow flowers in the first image
B. Track in the second image
C. Green background in the first image
D. Train in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is more affected by blur?
A. Yellow flowers in the first image
B. Track in the second image
C. Green background in the first image
D. Train in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more affected by blur?\nA. Yellow flowers in the first image\nB. Track in the second image\nC. Green background in the first image\nD. Train in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5043,[Response]: B.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 230:  23%|▏| 231/1000 [02:40<07
[Running Accuracy]: 0.5022,[Response]: B.<|endoftext|>, [Correct Ans]: Green background in the first image, , [Prog]: 2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is more affected by blur?\nA. Yellow flowers in the first image\nB. Track in the second image\nC. Green background in the first image\nD. Train in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the first image affected by underexposure?
A. More severe
B. Slightly more
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the first image affected by underexposure?
A. More severe
B. Slightly more
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the first image affected by underexposure?\nA. More severe\nB. Slightly more\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5022,[Response]: B.<|endoftext|>, [Correct Ans]: Green background in the first image, , [Prog]: 2
[Running Accuracy]: 0.5000,[Response]: C.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 232:  23%|▏| 232/1000 [02
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the first image affected by underexposure?\nA. More severe\nB. Slightly more\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the clarity of the first image lower than that of the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the clarity of the first image lower than that of the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the clarity of the first image lower than that of the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5000,[Response]: C.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 232:  23%|▏| 233/1000 [02
[Running Accuracy]: 0.5021,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 233:  23%|▏| 233/1000 [02:41<07:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the clarity of the first image lower than that of the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the noise in the first image much more obvious than in the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the noise in the first image much more obvious than in the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the noise in the first image much more obvious than in the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5021,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 233:  23%|▏| 234/1000 [02:42<07:0
[Running Accuracy]: 0.5043,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 234:  23%|▏| 234/1000 [02:42<07:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the noise in the first image much more obvious than in the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the clarity of the second image significantly higher than that of the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the clarity of the second image significantly higher than that of the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the clarity of the second image significantly higher than that of the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5043,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 234:  24%|▏| 235/1000 [02:43<07:1
[Running Accuracy]: 0.5021,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 235:  24%|▏| 235/1000 [02:43<07:19
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the clarity of the second image significantly higher than that of the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is overexposure more severe in the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is overexposure more severe in the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is overexposure more severe in the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5021,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 235:  24%|▏| 236/1000 [02:43<07:12
[Running Accuracy]: 0.5042,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 236:  24%|▏| 236/1000 [02:43<07:12
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is overexposure more severe in the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both images not very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both images not very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both images not very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5042,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 236:  24%|▏| 237/1000 [02:44<07:11
[Running Accuracy]: 0.5063,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 237:  24%|▏| 237/1000 [02:44<07:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both images not very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the motion blur in the first image more severe than in the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the motion blur in the first image more severe than in the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the motion blur in the first image more severe than in the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5063,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 237:  24%|▏| 238/1000 [02:44<07:1
[Running Accuracy]: 0.5084,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 238:  24%|▏| 238/1000 [02:44<07:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the motion blur in the first image more severe than in the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the sky in the second image more affected by overexposure than the sky in the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the sky in the second image more affected by overexposure than the sky in the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the sky in the second image more affected by overexposure than the sky in the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5084,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 238:  24%|▏| 239/1000 [02:45<08:0
[Running Accuracy]: 0.5063,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 239:  24%|▏| 239/1000 [02:45<08:08
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the sky in the second image more affected by overexposure than the sky in the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the detail texture of the second image clearer than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the detail texture of the second image clearer than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the detail texture of the second image clearer than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5063,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 239:  24%|▏| 240/1000 [02:46<07:44
[Running Accuracy]: 0.5083,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 240:  24%|▏| 240/1000 [02:46<07:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the detail texture of the second image clearer than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5083,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 240:  24%|▏| 241/1000 [02:46<07:5
[Running Accuracy]: 0.5062,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 241:  24%|▏| 241/1000 [02:46<07:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both images not genuine?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both images not genuine?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both images not genuine?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5062,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 241:  24%|▏| 242/1000 [02:47<07:3
[Running Accuracy]: 0.5083,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 242:  24%|▏| 242/1000 [02:47<07:33
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both images not genuine?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the sharpness of the first image higher than that of the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the sharpness of the first image higher than that of the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the sharpness of the first image higher than that of the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5083,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 242:  24%|▏| 243/1000 [02:47<07:26
[Running Accuracy]: 0.5062,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 243:  24%|▏| 243/1000 [02:47<07:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the sharpness of the first image higher than that of the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is suffering from more severe motion blur?
A. The bird's beak in the second image
B. The bird's tail in the second image
C. The woman's body in the first image
D. The woman's face in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is suffering from more severe motion blur?
A. The bird's beak in the second image
B. The bird's tail in the second image
C. The woman's body in the first image
D. The woman's face in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is suffering from more severe motion blur?\nA. The bird's beak in the second image\nB. The bird's tail in the second image\nC. The woman's body in the first image\nD. The woman's face in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5062,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 243:  24%|▏| 244/1000 [02:48<07:1
[Running Accuracy]: 0.5082,[Response]: A.<|endoftext|>, [Correct Ans]: The bird's beak in the second image, , [Prog]: 2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is suffering from more severe motion blur?\nA. The bird's beak in the second image\nB. The bird's tail in the second image\nC. The woman's body in the first image\nD. The woman's face in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the noise situation in the second image?
A. Similar
B. More serious
C. Slighter
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the noise situation in the second image?
A. Similar
B. More serious
C. Slighter
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the noise situation in the second image?\nA. Similar\nB. More serious\nC. Slighter\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5082,[Response]: A.<|endoftext|>, [Correct Ans]: The bird's beak in the second image, , [Prog]: 2
[Running Accuracy]: 0.5102,[Response]: B.<|endoftext|>, [Correct Ans]: More serious, , [Prog]: 245:  24%|▏| 245/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the noise situation in the second image?\nA. Similar\nB. More serious\nC. Slighter\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How is the focus of the second image compared to the first image?
A. Much worse
B. About the same
C. Much better
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How is the focus of the second image compared to the first image?
A. Much worse
B. About the same
C. Much better
Answer with the option's letter from the given choices directly.

prompts: [["How is the focus of the second image compared to the first image?\nA. Much worse\nB. About the same\nC. Much better\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5102,[Response]: B.<|endoftext|>, [Correct Ans]: More serious, , [Prog]: 245:  25%|▏| 246/1000 [0
[Running Accuracy]: 0.5122,[Response]: A.<|endoftext|>, [Correct Ans]: Much worse, , [Prog]: 246:  25%|▏| 246/1000 [02:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How is the focus of the second image compared to the first image?\nA. Much worse\nB. About the same\nC. Much better\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how does the color richness of the second image compare?
A. Much poorer
B. Much richer
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how does the color richness of the second image compare?
A. Much poorer
B. Much richer
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how does the color richness of the second image compare?\nA. Much poorer\nB. Much richer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5122,[Response]: A.<|endoftext|>, [Correct Ans]: Much worse, , [Prog]: 246:  25%|▏| 247/1000 [02:
[Running Accuracy]: 0.5142,[Response]: B.<|endoftext|>, [Correct Ans]: Much richer, , [Prog]: 247:  25%|▏| 247/1000 [02
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how does the color richness of the second image compare?\nA. Much poorer\nB. Much richer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the subject's details and textures in the second image?
A. More blurry
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the subject's details and textures in the second image?
A. More blurry
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the subject's details and textures in the second image?\nA. More blurry\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5142,[Response]: B.<|endoftext|>, [Correct Ans]: Much richer, , [Prog]: 247:  25%|▏| 248/1000 [02
[Running Accuracy]: 0.5121,[Response]: C.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 248:  25%|▏| 248/1000 [02
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the subject's details and textures in the second image?\nA. More blurry\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image richer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image richer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image richer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5121,[Response]: C.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 248:  25%|▏| 249/1000 [02
[Running Accuracy]: 0.5100,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 249:  25%|▏| 249/1000 [02:51<07:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image richer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the second image more realistic than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the second image more realistic than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the second image more realistic than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.6719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5100,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 249:  25%|▎| 250/1000 [02:51<07:1
[Running Accuracy]: 0.5080,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 250:  25%|▎| 250/1000 [02:51<07:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the second image more realistic than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image much richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image much richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image much richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5080,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 250:  25%|▎| 251/1000 [02:52<07:0
[Running Accuracy]: 0.5100,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 251:  25%|▎| 251/1000 [02:52<07:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image much richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which of the following is not a more serious problem in the second image compared to the first image?
A. Blurry
B. Low light
C. Underexposed
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which of the following is not a more serious problem in the second image compared to the first image?
A. Blurry
B. Low light
C. Underexposed
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following is not a more serious problem in the second image compared to the first image?\nA. Blurry\nB. Low light\nC. Underexposed\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5100,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 251:  25%|▎| 252/1000 [02:53<07:5
[Running Accuracy]: 0.5079,[Response]: C.<|endoftext|>, [Correct Ans]: Blurry, , [Prog]: 252:  25%|▎| 252/1000 [02:53<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which of the following is not a more serious problem in the second image compared to the first image?\nA. Blurry\nB. Low light\nC. Underexposed\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5079,[Response]: C.<|endoftext|>, [Correct Ans]: Blurry, , [Prog]: 252:  25%|▎| 253/1000 [02:53<0
[Running Accuracy]: 0.5099,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 253:  25%|▎| 253/1000 [02:53<07:54
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the impact of motion blur on the first image?
A. Similar
B. Much more severe
C. Slightly more severe
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the impact of motion blur on the first image?
A. Similar
B. Much more severe
C. Slightly more severe
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the impact of motion blur on the first image?\nA. Similar\nB. Much more severe\nC. Slightly more severe\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5099,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 253:  25%|▎| 254/1000 [02:54<07:48
[Running Accuracy]: 0.5118,[Response]: B.<|endoftext|>, [Correct Ans]: Much more severe, , [Prog]: 254:  25%|▎| 254/100
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the impact of motion blur on the first image?\nA. Similar\nB. Much more severe\nC. Slightly more severe\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the first image affected by noise?
A. Similar
B. Lighter
C. More severe
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the first image affected by noise?
A. Similar
B. Lighter
C. More severe
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the first image affected by noise?\nA. Similar\nB. Lighter\nC. More severe\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5118,[Response]: B.<|endoftext|>, [Correct Ans]: Much more severe, , [Prog]: 254:  26%|▎| 255/100
[Running Accuracy]: 0.5098,[Response]: C.<|endoftext|>, [Correct Ans]: Lighter, , [Prog]: 255:  26%|▎| 255/1000 [02:55<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the first image affected by noise?\nA. Similar\nB. Lighter\nC. More severe\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>In comparison to the first image, how is the focusing situation in the second image?
A. Much better
B. Much worse
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:In comparison to the first image, how is the focusing situation in the second image?
A. Much better
B. Much worse
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["In comparison to the first image, how is the focusing situation in the second image?\nA. Much better\nB. Much worse\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5098,[Response]: C.<|endoftext|>, [Correct Ans]: Lighter, , [Prog]: 255:  26%|▎| 256/1000 [02:55<
[Running Accuracy]: 0.5117,[Response]: B.<|endoftext|>, [Correct Ans]: Much worse, , [Prog]: 256:  26%|▎| 256/1000 [02:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>In comparison to the first image, how is the focusing situation in the second image?\nA. Much better\nB. Much worse\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the lighting in the second image?
A. Much worse
B. Much better
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the lighting in the second image?
A. Much worse
B. Much better
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the lighting in the second image?\nA. Much worse\nB. Much better\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5117,[Response]: B.<|endoftext|>, [Correct Ans]: Much worse, , [Prog]: 256:  26%|▎| 257/1000 [02:
[Running Accuracy]: 0.5097,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 257:  26%|▎| 257/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the lighting in the second image?\nA. Much worse\nB. Much better\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the second image affected by low light?
A. More severe
B. Slightly less
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the second image affected by low light?
A. More severe
B. Slightly less
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the second image affected by low light?\nA. More severe\nB. Slightly less\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5097,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 257:  26%|▎| 258/1000 
[Running Accuracy]: 0.5116,[Response]: B.<|endoftext|>, [Correct Ans]: Slightly less, , [Prog]: 258:  26%|▎| 258/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the second image affected by low light?\nA. More severe\nB. Slightly less\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the focus of the second image compare to that of the first image?
A. Similar
B. Slightly better
C. Slightly worse
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the focus of the second image compare to that of the first image?
A. Similar
B. Slightly better
C. Slightly worse
Answer with the option's letter from the given choices directly.

prompts: [["How does the focus of the second image compare to that of the first image?\nA. Similar\nB. Slightly better\nC. Slightly worse\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5000], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5116,[Response]: B.<|endoftext|>, [Correct Ans]: Slightly less, , [Prog]: 258:  26%|▎| 259/1000 [
[Running Accuracy]: 0.5097,[Response]: C.<|endoftext|>, [Correct Ans]: Slightly better, , [Prog]: 259:  26%|▎| 259/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the focus of the second image compare to that of the first image?\nA. Similar\nB. Slightly better\nC. Slightly worse\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which of the following is a more severe distortion in the second image compared to the first image?
A. Out of focus
B. Low light
C. Blurry
D. Color distortion
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which of the following is a more severe distortion in the second image compared to the first image?
A. Out of focus
B. Low light
C. Blurry
D. Color distortion
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following is a more severe distortion in the second image compared to the first image?\nA. Out of focus\nB. Low light\nC. Blurry\nD. Color distortion\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5000], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5097,[Response]: C.<|endoftext|>, [Correct Ans]: Slightly better, , [Prog]: 259:  26%|▎| 260/1000
[Running Accuracy]: 0.5115,[Response]: A.<|endoftext|>, [Correct Ans]: Out of focus, , [Prog]: 260:  26%|▎| 260/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which of the following is a more severe distortion in the second image compared to the first image?\nA. Out of focus\nB. Low light\nC. Blurry\nD. Color distortion\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how is the lighting situation in the first image?
A. about the same
B. worse
C. better
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how is the lighting situation in the first image?
A. about the same
B. worse
C. better
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the lighting situation in the first image?\nA. about the same\nB. worse\nC. better\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5115,[Response]: A.<|endoftext|>, [Correct Ans]: Out of focus, , [Prog]: 260:  26%|▎| 261/1000 [0
[Running Accuracy]: 0.5134,[Response]: C.<|endoftext|>, [Correct Ans]: better, , [Prog]: 261:  26%|▎| 261/1000 [02:58<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how is the lighting situation in the first image?\nA. about the same\nB. worse\nC. better\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>There are obvious distortions in the two images, which of the following is NOT included?
A. Blur
B. Noise
C. Overexposure
D. Out of focus
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:There are obvious distortions in the two images, which of the following is NOT included?
A. Blur
B. Noise
C. Overexposure
D. Out of focus
Answer with the option's letter from the given choices directly.

prompts: [["There are obvious distortions in the two images, which of the following is NOT included?\nA. Blur\nB. Noise\nC. Overexposure\nD. Out of focus\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5134,[Response]: C.<|endoftext|>, [Correct Ans]: better, , [Prog]: 261:  26%|▎| 262/1000 [02:58<0
[Running Accuracy]: 0.5153,[Response]: C.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 262:  26%|▎| 262/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>There are obvious distortions in the two images, which of the following is NOT included?\nA. Blur\nB. Noise\nC. Overexposure\nD. Out of focus\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are there severe motion blur in both images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are there severe motion blur in both images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are there severe motion blur in both images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5153,[Response]: C.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 262:  26%|▎| 263/1000 [0
[Running Accuracy]: 0.5133,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 263:  26%|▎| 263/1000 [02:59<06:52
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are there severe motion blur in both images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area has clearer details and textures?
A. The woman's face in the second image
B. The blanket in the second image
C. The grassland background in the first image
D. The dog's fur in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area has clearer details and textures?
A. The woman's face in the second image
B. The blanket in the second image
C. The grassland background in the first image
D. The dog's fur in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area has clearer details and textures?\nA. The woman's face in the second image\nB. The blanket in the second image\nC. The grassland background in the first image\nD. The dog's fur in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5000], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5133,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 263:  26%|▎| 264/1000 [03:00<06:52
[Running Accuracy]: 0.5114,[Response]: A.<|endoftext|>, [Correct Ans]: The dog's fur in the first image, , [Prog]: 264:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area has clearer details and textures?\nA. The woman's face in the second image\nB. The blanket in the second image\nC. The grassland background in the first image\nD. The dog's fur in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the lighting condition in the second image compare to the first image?
A. slightly worse
B. slightly better
C. about the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the lighting condition in the second image compare to the first image?
A. slightly worse
B. slightly better
C. about the same
Answer with the option's letter from the given choices directly.

prompts: [["How does the lighting condition in the second image compare to the first image?\nA. slightly worse\nB. slightly better\nC. about the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5114,[Response]: A.<|endoftext|>, [Correct Ans]: The dog's fur in the first image, , [Prog]: 264:
[Running Accuracy]: 0.5094,[Response]: B.<|endoftext|>, [Correct Ans]: slightly worse, , [Prog]: 265:  26%|▎| 265/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the lighting condition in the second image compare to the first image?\nA. slightly worse\nB. slightly better\nC. about the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the focus of the second image?
A. Much worse
B. Much better
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the focus of the second image?
A. Much worse
B. Much better
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the focus of the second image?\nA. Much worse\nB. Much better\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5094,[Response]: B.<|endoftext|>, [Correct Ans]: slightly worse, , [Prog]: 265:  27%|▎| 266/1000 
[Running Accuracy]: 0.5113,[Response]: A.<|endoftext|>, [Correct Ans]: Much worse, , [Prog]: 266:  27%|▎| 266/1000 [03:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the focus of the second image?\nA. Much worse\nB. Much better\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the fine details and textures in the second image?
A. Clearer
B. About the same
C. More blurry
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the fine details and textures in the second image?
A. Clearer
B. About the same
C. More blurry
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the fine details and textures in the second image?\nA. Clearer\nB. About the same\nC. More blurry\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5113,[Response]: A.<|endoftext|>, [Correct Ans]: Much worse, , [Prog]: 266:  27%|▎| 267/1000 [03:
[Running Accuracy]: 0.5131,[Response]: A.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 267:  27%|▎| 267/1000 [03:02<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the fine details and textures in the second image?\nA. Clearer\nB. About the same\nC. More blurry\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which of the following is not a more serious problem in the second image compared to the first image?
A. Low light
B. Blurry
C. Underexposed
D. Low resolution
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which of the following is not a more serious problem in the second image compared to the first image?
A. Low light
B. Blurry
C. Underexposed
D. Low resolution
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following is not a more serious problem in the second image compared to the first image?\nA. Low light\nB. Blurry\nC. Underexposed\nD. Low resolution\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.5131,[Response]: A.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 267:  27%|▎| 268/1000 [03:02<
[Running Accuracy]: 0.5112,[Response]: D.<|endoftext|>, [Correct Ans]: Blurry, , [Prog]: 268:  27%|▎| 268/1000 [03:02<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which of the following is not a more serious problem in the second image compared to the first image?\nA. Low light\nB. Blurry\nC. Underexposed\nD. Low resolution\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the second image more realistic than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the second image more realistic than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the second image more realistic than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-29.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5112,[Response]: D.<|endoftext|>, [Correct Ans]: Blurry, , [Prog]: 268:  27%|▎| 269/1000 [03:03<0
[Running Accuracy]: 0.5130,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 269:  27%|▎| 269/1000 [03:03<07:27
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the second image more realistic than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both images very realistic?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both images very realistic?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both images very realistic?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5130,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 269:  27%|▎| 270/1000 [03:03<07:12
[Running Accuracy]: 0.5148,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 270:  27%|▎| 270/1000 [03:03<07:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both images very realistic?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which of the following is not included in the obvious severe distortion of the second image compared to the first image?
A. Blur
B. Low light
C. Out of focus
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which of the following is not included in the obvious severe distortion of the second image compared to the first image?
A. Blur
B. Low light
C. Out of focus
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following is not included in the obvious severe distortion of the second image compared to the first image?\nA. Blur\nB. Low light\nC. Out of focus\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5148,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 270:  27%|▎| 271/1000 [03:04<08:1
[Running Accuracy]: 0.5166,[Response]: B.<|endoftext|>, [Correct Ans]: Low light, , [Prog]: 271:  27%|▎| 271/1000 [03:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which of the following is not included in the obvious severe distortion of the second image compared to the first image?\nA. Blur\nB. Low light\nC. Out of focus\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is most affected by overexposure?
A. Area in the first image
B. Roof of the building in the second image
C. Athlete in the first image
D. Sky in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is most affected by overexposure?
A. Area in the first image
B. Roof of the building in the second image
C. Athlete in the first image
D. Sky in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is most affected by overexposure?\nA. Area in the first image\nB. Roof of the building in the second image\nC. Athlete in the first image\nD. Sky in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.5166,[Response]: B.<|endoftext|>, [Correct Ans]: Low light, , [Prog]: 271:  27%|▎| 272/1000 [03:0
[Running Accuracy]: 0.5184,[Response]: D.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 272:  27%|▎| 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is most affected by overexposure?\nA. Area in the first image\nB. Roof of the building in the second image\nC. Athlete in the first image\nD. Sky in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Lower
B. Higher
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Lower
B. Higher
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Lower\nB. Higher\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5184,[Response]: D.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 272:  27%|▎| 
[Running Accuracy]: 0.5165,[Response]: C.<|endoftext|>, [Correct Ans]: Lower, , [Prog]: 273:  27%|▎| 273/1000 [03:05<07
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Lower\nB. Higher\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is more severely affected by exposure issues?
A. Frame in the lower right corner of the second image
B. Building in the first image
C. Frame in the upper left corner of the second image
D. Vehicle in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is more severely affected by exposure issues?
A. Frame in the lower right corner of the second image
B. Building in the first image
C. Frame in the upper left corner of the second image
D. Vehicle in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more severely affected by exposure issues?\nA. Frame in the lower right corner of the second image\nB. Building in the first image\nC. Frame in the upper left corner of the second image\nD. Vehicle in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5165,[Response]: C.<|endoftext|>, [Correct Ans]: Lower, , [Prog]: 273:  27%|▎| 274/1000 [03:06<07
[Running Accuracy]: 0.5146,[Response]: A.<|endoftext|>, [Correct Ans]: Vehicle in the first image, , [Prog]: 274:  27%|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is more severely affected by exposure issues?\nA. Frame in the lower right corner of the second image\nB. Building in the first image\nC. Frame in the upper left corner of the second image\nD. Vehicle in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how accurate is the second image?
A. A lot of real
B. Almost the same
C. A lot of fake
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how accurate is the second image?
A. A lot of real
B. Almost the same
C. A lot of fake
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how accurate is the second image?\nA. A lot of real\nB. Almost the same\nC. A lot of fake\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5146,[Response]: A.<|endoftext|>, [Correct Ans]: Vehicle in the first image, , [Prog]: 274:  28%|
[Running Accuracy]: 0.5164,[Response]: C.<|endoftext|>, [Correct Ans]: A lot of fake, , [Prog]: 275:  28%|▎| 275/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how accurate is the second image?\nA. A lot of real\nB. Almost the same\nC. A lot of fake\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the details and textures in the first image clearer than those in the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the details and textures in the first image clearer than those in the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the details and textures in the first image clearer than those in the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5164,[Response]: C.<|endoftext|>, [Correct Ans]: A lot of fake, , [Prog]: 275:  28%|▎| 276/1000 [
[Running Accuracy]: 0.5145,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 276:  28%|▎| 276/1000 [03:07<07:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the details and textures in the first image clearer than those in the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared with the second image, how is the sharpness of the first image?
A. Similar
B. Higher
C. Lower
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared with the second image, how is the sharpness of the first image?
A. Similar
B. Higher
C. Lower
Answer with the option's letter from the given choices directly.

prompts: [["Compared with the second image, how is the sharpness of the first image?\nA. Similar\nB. Higher\nC. Lower\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5145,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 276:  28%|▎| 277/1000 [03:08<07:3
[Running Accuracy]: 0.5126,[Response]: C.<|endoftext|>, [Correct Ans]: Higher, , [Prog]: 277:  28%|▎| 277/1000 [03:08<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared with the second image, how is the sharpness of the first image?\nA. Similar\nB. Higher\nC. Lower\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is more severely affected by blurring?
A. Background of the second image
B. Table in front of the second image
C. Grass in the first image
D. Snowy mountain in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is more severely affected by blurring?
A. Background of the second image
B. Table in front of the second image
C. Grass in the first image
D. Snowy mountain in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more severely affected by blurring?\nA. Background of the second image\nB. Table in front of the second image\nC. Grass in the first image\nD. Snowy mountain in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5126,[Response]: C.<|endoftext|>, [Correct Ans]: Higher, , [Prog]: 277:  28%|▎| 278/1000 [03:09<0
[Running Accuracy]: 0.5144,[Response]: A.<|endoftext|>, [Correct Ans]: Background of the second image, , [Prog]: 278:  
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is more severely affected by blurring?\nA. Background of the second image\nB. Table in front of the second image\nC. Grass in the first image\nD. Snowy mountain in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the second image more realistic than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the second image more realistic than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the second image more realistic than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5144,[Response]: A.<|endoftext|>, [Correct Ans]: Background of the second image, , [Prog]: 278:  
[Running Accuracy]: 0.5161,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 279:  28%|▎| 279/1000 [03:09<07:32
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the second image more realistic than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area has clearer fine textures?
A. The ground in the first image
B. The wardrobe in the second image
C. The fur of the cat in the second image
D. The legs of the athlete in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area has clearer fine textures?
A. The ground in the first image
B. The wardrobe in the second image
C. The fur of the cat in the second image
D. The legs of the athlete in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area has clearer fine textures?\nA. The ground in the first image\nB. The wardrobe in the second image\nC. The fur of the cat in the second image\nD. The legs of the athlete in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5161,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 279:  28%|▎| 280/1000 [03:10<07:29
[Running Accuracy]: 0.5179,[Response]: C.<|endoftext|>, [Correct Ans]: The fur of the cat in the second image, , [Prog]
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area has clearer fine textures?\nA. The ground in the first image\nB. The wardrobe in the second image\nC. The fur of the cat in the second image\nD. The legs of the athlete in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the focusing of the second image compare to the first image?
A. Much better
B. Much worse
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the focusing of the second image compare to the first image?
A. Much better
B. Much worse
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["How does the focusing of the second image compare to the first image?\nA. Much better\nB. Much worse\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5179,[Response]: C.<|endoftext|>, [Correct Ans]: The fur of the cat in the second image, , [Prog]
[Running Accuracy]: 0.5160,[Response]: B.<|endoftext|>, [Correct Ans]: Much better, , [Prog]: 281:  28%|▎| 281/1000 [03
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the focusing of the second image compare to the first image?\nA. Much better\nB. Much worse\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the lighting in the second image better than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the lighting in the second image better than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the lighting in the second image better than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5160,[Response]: B.<|endoftext|>, [Correct Ans]: Much better, , [Prog]: 281:  28%|▎| 282/1000 [03
[Running Accuracy]: 0.5177,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 282:  28%|▎| 282/1000 [03:11<08:20
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the lighting in the second image better than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the detail texture clarity of the second image better than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the detail texture clarity of the second image better than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the detail texture clarity of the second image better than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5177,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 282:  28%|▎| 283/1000 [03:12<08:12
[Running Accuracy]: 0.5194,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 283:  28%|▎| 283/1000 [03:12<08:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the detail texture clarity of the second image better than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is more severely affected by blurring?
A. The bird in the second image
B. The green background in the second image
C. The circular background in the first image
D. The human body in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is more severely affected by blurring?
A. The bird in the second image
B. The green background in the second image
C. The circular background in the first image
D. The human body in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more severely affected by blurring?\nA. The bird in the second image\nB. The green background in the second image\nC. The circular background in the first image\nD. The human body in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5194,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 283:  28%|▎| 284/1000 [03:13<08:0
[Running Accuracy]: 0.5211,[Response]: B.<|endoftext|>, [Correct Ans]: The green background in the second image, , [Pro
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is more severely affected by blurring?\nA. The bird in the second image\nB. The green background in the second image\nC. The circular background in the first image\nD. The human body in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the detail texture of the duck in figure two clearer than the detail texture of the dog in figure one?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the detail texture of the duck in figure two clearer than the detail texture of the dog in figure one?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the detail texture of the duck in figure two clearer than the detail texture of the dog in figure one?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5211,[Response]: B.<|endoftext|>, [Correct Ans]: The green background in the second image, , [Pro
[Running Accuracy]: 0.5193,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 285:  28%|▎| 285/1000 [03:13<07:52
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the detail texture of the duck in figure two clearer than the detail texture of the dog in figure one?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the clarity of the first image higher than that of the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the clarity of the first image higher than that of the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the clarity of the first image higher than that of the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5193,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 285:  29%|▎| 286/1000 [03:14<07:43
[Running Accuracy]: 0.5175,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 286:  29%|▎| 286/1000 [03:14<07:43
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the clarity of the first image higher than that of the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is more affected by motion blur?
A. Red blanket in the second image
B. Yellow blanket in the second image
C. Little girl in the bottom left corner of the first image
D. Seats in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is more affected by motion blur?
A. Red blanket in the second image
B. Yellow blanket in the second image
C. Little girl in the bottom left corner of the first image
D. Seats in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more affected by motion blur?\nA. Red blanket in the second image\nB. Yellow blanket in the second image\nC. Little girl in the bottom left corner of the first image\nD. Seats in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5175,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 286:  29%|▎| 287/1000 [03:14<07:37
[Running Accuracy]: 0.5157,[Response]: C.<|endoftext|>, [Correct Ans]: Seats in the first image, , [Prog]: 287:  29%|▎|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is more affected by motion blur?\nA. Red blanket in the second image\nB. Yellow blanket in the second image\nC. Little girl in the bottom left corner of the first image\nD. Seats in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is only the first image showing significant underexposure?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is only the first image showing significant underexposure?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is only the first image showing significant underexposure?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5157,[Response]: C.<|endoftext|>, [Correct Ans]: Seats in the first image, , [Prog]: 287:  29%|▎|
[Running Accuracy]: 0.5139,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 288:  29%|▎| 288/1000 [03:15<07:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is only the first image showing significant underexposure?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the noise in the first image more severe than in the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the noise in the first image more severe than in the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the noise in the first image more severe than in the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5139,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 288:  29%|▎| 289/1000 [03:16<07:2
[Running Accuracy]: 0.5156,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 289:  29%|▎| 289/1000 [03:16<07:24
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the noise in the first image more severe than in the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the motion blur in the second image more severe than in the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the motion blur in the second image more severe than in the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the motion blur in the second image more severe than in the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5156,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 289:  29%|▎| 290/1000 [03:16<07:45
[Running Accuracy]: 0.5138,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 290:  29%|▎| 290/1000 [03:16<07:45
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the motion blur in the second image more severe than in the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the exposure of the second image much better than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the exposure of the second image much better than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the exposure of the second image much better than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5138,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 290:  29%|▎| 291/1000 [03:17<07:30
[Running Accuracy]: 0.5155,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 291:  29%|▎| 291/1000 [03:17<07:30
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the exposure of the second image much better than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are there more distortion issues in the second image than in the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are there more distortion issues in the second image than in the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are there more distortion issues in the second image than in the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5155,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 291:  29%|▎| 292/1000 [03:18<07:15
[Running Accuracy]: 0.5171,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 292:  29%|▎| 292/1000 [03:18<07:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are there more distortion issues in the second image than in the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the detailed texture of the main subject in the second image clearer than that in the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the detailed texture of the main subject in the second image clearer than that in the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the detailed texture of the main subject in the second image clearer than that in the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5171,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 292:  29%|▎| 293/1000 [03:18<07:1
[Running Accuracy]: 0.5188,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 293:  29%|▎| 293/1000 [03:18<07:16
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the detailed texture of the main subject in the second image clearer than that in the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the noise in the first image more severe than in the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the noise in the first image more severe than in the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the noise in the first image more severe than in the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5188,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 293:  29%|▎| 294/1000 [03:19<07:16
[Running Accuracy]: 0.5170,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 294:  29%|▎| 294/1000 [03:19<07:16
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the noise in the first image more severe than in the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the focus on the subject in the first image better than in the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the focus on the subject in the first image better than in the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the focus on the subject in the first image better than in the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5170,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 294:  30%|▎| 295/1000 [03:19<07:19
[Running Accuracy]: 0.5153,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 295:  30%|▎| 295/1000 [03:19<07:19
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the focus on the subject in the first image better than in the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Comparing to the first image, how is the fidelity of the second image?
A. about the same
B. more realistic
C. less realistic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Comparing to the first image, how is the fidelity of the second image?
A. about the same
B. more realistic
C. less realistic
Answer with the option's letter from the given choices directly.

prompts: [["Comparing to the first image, how is the fidelity of the second image?\nA. about the same\nB. more realistic\nC. less realistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5153,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 295:  30%|▎| 296/1000 [03:20<07:23
[Running Accuracy]: 0.5169,[Response]: C.<|endoftext|>, [Correct Ans]: less realistic, , [Prog]: 296:  30%|▎| 296/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Comparing to the first image, how is the fidelity of the second image?\nA. about the same\nB. more realistic\nC. less realistic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images not sharp?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images not sharp?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images not sharp?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5169,[Response]: C.<|endoftext|>, [Correct Ans]: less realistic, , [Prog]: 296:  30%|▎| 297/1000 
[Running Accuracy]: 0.5152,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 297:  30%|▎| 297/1000 [03:21<07:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images not sharp?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the lighting in the second image?
A. Similar
B. More adequate
C. Less adequate
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the lighting in the second image?
A. Similar
B. More adequate
C. Less adequate
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the lighting in the second image?\nA. Similar\nB. More adequate\nC. Less adequate\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5152,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 297:  30%|▎| 298/1000 [03:21<07:1
[Running Accuracy]: 0.5168,[Response]: C.<|endoftext|>, [Correct Ans]: Less adequate, , [Prog]: 298:  30%|▎| 298/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the lighting in the second image?\nA. Similar\nB. More adequate\nC. Less adequate\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5168,[Response]: C.<|endoftext|>, [Correct Ans]: Less adequate, , [Prog]: 298:  30%|▎| 299/1000 [
[Running Accuracy]: 0.5184,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 299:  30%|▎| 299/1000 [03:22<07:11
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the richness of colors in the second image?
A. Richer
B. Less rich
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the richness of colors in the second image?
A. Richer
B. Less rich
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the richness of colors in the second image?\nA. Richer\nB. Less rich\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5184,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 299:  30%|▎| 300/1000 [03:23<08:10
[Running Accuracy]: 0.5167,[Response]: B.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 300:  30%|▎| 300/1000 [03:23<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the richness of colors in the second image?\nA. Richer\nB. Less rich\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which kind of distortion issue is not present in these two images?
A. overexposure
B. noise
C. out-of-focus
D. motion blur
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which kind of distortion issue is not present in these two images?
A. overexposure
B. noise
C. out-of-focus
D. motion blur
Answer with the option's letter from the given choices directly.

prompts: [["Which kind of distortion issue is not present in these two images?\nA. overexposure\nB. noise\nC. out-of-focus\nD. motion blur\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5167,[Response]: B.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 300:  30%|▎| 301/1000 [03:24<0
[Running Accuracy]: 0.5150,[Response]: C.<|endoftext|>, [Correct Ans]: noise, , [Prog]: 301:  30%|▎| 301/1000 [03:24<08
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which kind of distortion issue is not present in these two images?\nA. overexposure\nB. noise\nC. out-of-focus\nD. motion blur\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the composition of the second image compare to the first image?
A. Similar
B. Worse
C. Better
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the composition of the second image compare to the first image?
A. Similar
B. Worse
C. Better
Answer with the option's letter from the given choices directly.

prompts: [["How does the composition of the second image compare to the first image?\nA. Similar\nB. Worse\nC. Better\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5150,[Response]: C.<|endoftext|>, [Correct Ans]: noise, , [Prog]: 301:  30%|▎| 302/1000 [03:24<08
[Running Accuracy]: 0.5166,[Response]: B.<|endoftext|>, [Correct Ans]: Worse, , [Prog]: 302:  30%|▎| 302/1000 [03:24<08
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the composition of the second image compare to the first image?\nA. Similar\nB. Worse\nC. Better\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. ground in the first image
B. person on the left in the second image
C. tent on the right in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. ground in the first image
B. person on the left in the second image
C. tent on the right in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. ground in the first image\nB. person on the left in the second image\nC. tent on the right in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5166,[Response]: B.<|endoftext|>, [Correct Ans]: Worse, , [Prog]: 302:  30%|▎| 303/1000 [03:25<07
[Running Accuracy]: 0.5182,[Response]: C.<|endoftext|>, [Correct Ans]: tent on the right in the second image, , [Prog]:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. ground in the first image\nB. person on the left in the second image\nC. tent on the right in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Relative to the first image, how is the clarity of the second image?
A. Clearer
B. More blurry
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Relative to the first image, how is the clarity of the second image?
A. Clearer
B. More blurry
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Relative to the first image, how is the clarity of the second image?\nA. Clearer\nB. More blurry\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5182,[Response]: C.<|endoftext|>, [Correct Ans]: tent on the right in the second image, , [Prog]:
[Running Accuracy]: 0.5197,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 304:  30%|▎| 304/1000 [03
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Relative to the first image, how is the clarity of the second image?\nA. Clearer\nB. More blurry\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the texture detail of the second image?
A. Richer
B. About the same
C. Less rich
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the texture detail of the second image?
A. Richer
B. About the same
C. Less rich
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the texture detail of the second image?\nA. Richer\nB. About the same\nC. Less rich\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5197,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 304:  30%|▎| 305/1000 [03
[Running Accuracy]: 0.5213,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 305:  30%|▎| 305/1000 [03:26<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the texture detail of the second image?\nA. Richer\nB. About the same\nC. Less rich\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the color richness of the second image?
A. Richer
B. Less rich
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the color richness of the second image?
A. Richer
B. Less rich
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the color richness of the second image?\nA. Richer\nB. Less rich\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5213,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 305:  31%|▎| 306/1000 [03:27<0
[Running Accuracy]: 0.5229,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 306:  31%|▎| 306/1000 [03:27<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the color richness of the second image?\nA. Richer\nB. Less rich\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5229,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 306:  31%|▎| 307/1000 [03:28<0
[Running Accuracy]: 0.5244,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 307:  31%|▎| 307/1000 [03:28<08:13
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the lighting of the second image?
A. Less sufficient
B. About the same
C. More sufficient
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the lighting of the second image?
A. Less sufficient
B. About the same
C. More sufficient
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the lighting of the second image?\nA. Less sufficient\nB. About the same\nC. More sufficient\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5244,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 307:  31%|▎| 308/1000 [03:28<07:54
[Running Accuracy]: 0.5227,[Response]: C.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 308:  31%|▎| 308/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the lighting of the second image?\nA. Less sufficient\nB. About the same\nC. More sufficient\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5227,[Response]: C.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 308:  31%|▎| 309/1000
[Running Accuracy]: 0.5243,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 309:  31%|▎| 309/1000 [03:29<07:41
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the authenticity of the second image?
A. similar
B. more realistic
C. less realistic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the authenticity of the second image?
A. similar
B. more realistic
C. less realistic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the authenticity of the second image?\nA. similar\nB. more realistic\nC. less realistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5243,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 309:  31%|▎| 310/1000 [03:30<07:31
[Running Accuracy]: 0.5226,[Response]: C.<|endoftext|>, [Correct Ans]: more realistic, , [Prog]: 310:  31%|▎| 310/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the authenticity of the second image?\nA. similar\nB. more realistic\nC. less realistic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part has the most severe overexposure issue?
A. Ground of the second image
B. Sky of the second image
C. Ground of the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part has the most severe overexposure issue?
A. Ground of the second image
B. Sky of the second image
C. Ground of the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the most severe overexposure issue?\nA. Ground of the second image\nB. Sky of the second image\nC. Ground of the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5226,[Response]: C.<|endoftext|>, [Correct Ans]: more realistic, , [Prog]: 310:  31%|▎| 311/1000 
[Running Accuracy]: 0.5241,[Response]: B.<|endoftext|>, [Correct Ans]: Sky of the second image, , [Prog]: 311:  31%|▎| 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part has the most severe overexposure issue?\nA. Ground of the second image\nB. Sky of the second image\nC. Ground of the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the realism of the second image compare to the first image?
A. More realistic
B. Less realistic
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the realism of the second image compare to the first image?
A. More realistic
B. Less realistic
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["How does the realism of the second image compare to the first image?\nA. More realistic\nB. Less realistic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5241,[Response]: B.<|endoftext|>, [Correct Ans]: Sky of the second image, , [Prog]: 311:  31%|▎| 
[Running Accuracy]: 0.5224,[Response]: B.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 312:  31%|▎| 312/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the realism of the second image compare to the first image?\nA. More realistic\nB. Less realistic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the texture detail of the second image?
A. similar
B. less rich
C. richer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the texture detail of the second image?
A. similar
B. less rich
C. richer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the texture detail of the second image?\nA. similar\nB. less rich\nC. richer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.6406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5224,[Response]: B.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 312:  31%|▎| 313/1000 
[Running Accuracy]: 0.5240,[Response]: C.<|endoftext|>, [Correct Ans]: richer, , [Prog]: 313:  31%|▎| 313/1000 [03:31<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the texture detail of the second image?\nA. similar\nB. less rich\nC. richer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the lighting in the second image?
A. similar
B. less adequate
C. more adequate
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the lighting in the second image?
A. similar
B. less adequate
C. more adequate
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the lighting in the second image?\nA. similar\nB. less adequate\nC. more adequate\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5240,[Response]: C.<|endoftext|>, [Correct Ans]: richer, , [Prog]: 313:  31%|▎| 314/1000 [03:32<0
[Running Accuracy]: 0.5255,[Response]: C.<|endoftext|>, [Correct Ans]: more adequate, , [Prog]: 314:  31%|▎| 314/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the lighting in the second image?\nA. similar\nB. less adequate\nC. more adequate\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the authenticity of the second image?
A. Similar
B. Less realistic
C. More realistic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the authenticity of the second image?
A. Similar
B. Less realistic
C. More realistic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the authenticity of the second image?\nA. Similar\nB. Less realistic\nC. More realistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5255,[Response]: C.<|endoftext|>, [Correct Ans]: more adequate, , [Prog]: 314:  32%|▎| 315/1000 [
[Running Accuracy]: 0.5238,[Response]: B.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 315:  32%|▎| 315/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the authenticity of the second image?\nA. Similar\nB. Less realistic\nC. More realistic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the lighting in the second image?
A. Less sufficient
B. About the same
C. More sufficient
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the lighting in the second image?
A. Less sufficient
B. About the same
C. More sufficient
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the lighting in the second image?\nA. Less sufficient\nB. About the same\nC. More sufficient\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5238,[Response]: B.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 315:  32%|▎| 316/1000 
[Running Accuracy]: 0.5253,[Response]: C.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 316:  32%|▎| 316/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the lighting in the second image?\nA. Less sufficient\nB. About the same\nC. More sufficient\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The characters in the second image
B. The light source of the liquor cabinet in the first image
C. The ground in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The characters in the second image
B. The light source of the liquor cabinet in the first image
C. The ground in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The characters in the second image\nB. The light source of the liquor cabinet in the first image\nC. The ground in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5253,[Response]: C.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 316:  32%|▎| 317/1000
[Running Accuracy]: 0.5268,[Response]: B.<|endoftext|>, [Correct Ans]: The light source of the liquor cabinet in the fi
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The characters in the second image\nB. The light source of the liquor cabinet in the first image\nC. The ground in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how are the texture details in the second image?
A. More abundant
B. About the same
C. Less abundant
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how are the texture details in the second image?
A. More abundant
B. About the same
C. Less abundant
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how are the texture details in the second image?\nA. More abundant\nB. About the same\nC. Less abundant\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5268,[Response]: B.<|endoftext|>, [Correct Ans]: The light source of the liquor cabinet in the fi
[Running Accuracy]: 0.5283,[Response]: A.<|endoftext|>, [Correct Ans]: More abundant, , [Prog]: 318:  32%|▎| 318/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how are the texture details in the second image?\nA. More abundant\nB. About the same\nC. Less abundant\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. Turtle in the first image
B. Shoes in the second image
C. Background light source in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. Turtle in the first image
B. Shoes in the second image
C. Background light source in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. Turtle in the first image\nB. Shoes in the second image\nC. Background light source in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5283,[Response]: A.<|endoftext|>, [Correct Ans]: More abundant, , [Prog]: 318:  32%|▎| 319/1000 [
[Running Accuracy]: 0.5298,[Response]: C.<|endoftext|>, [Correct Ans]: Background light source in the second image, , [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. Turtle in the first image\nB. Shoes in the second image\nC. Background light source in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the color vividness of the second image?
A. Similar
B. Less vivid
C. More vivid
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the color vividness of the second image?
A. Similar
B. Less vivid
C. More vivid
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the color vividness of the second image?\nA. Similar\nB. Less vivid\nC. More vivid\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5298,[Response]: C.<|endoftext|>, [Correct Ans]: Background light source in the second image, , [
[Running Accuracy]: 0.5281,[Response]: B.<|endoftext|>, [Correct Ans]: More vivid, , [Prog]: 320:  32%|▎| 320/1000 [03:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the color vividness of the second image?\nA. Similar\nB. Less vivid\nC. More vivid\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how rich is the color in the second image?
A. Less rich
B. About the same
C. Richer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how rich is the color in the second image?
A. Less rich
B. About the same
C. Richer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how rich is the color in the second image?\nA. Less rich\nB. About the same\nC. Richer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5281,[Response]: B.<|endoftext|>, [Correct Ans]: More vivid, , [Prog]: 320:  32%|▎| 321/1000 [03:
[Running Accuracy]: 0.5296,[Response]: C.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 321:  32%|▎| 321/1000 [03:36<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how rich is the color in the second image?\nA. Less rich\nB. About the same\nC. Richer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how does the color richness of the second image look?
A. Less rich
B. More rich
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how does the color richness of the second image look?
A. Less rich
B. More rich
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how does the color richness of the second image look?\nA. Less rich\nB. More rich\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5296,[Response]: C.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 321:  32%|▎| 322/1000 [03:37<0
[Running Accuracy]: 0.5311,[Response]: B.<|endoftext|>, [Correct Ans]: More rich, , [Prog]: 322:  32%|▎| 322/1000 [03:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how does the color richness of the second image look?\nA. Less rich\nB. More rich\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how authentic is the second image?
A. Less authentic
B. About the same
C. More authentic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how authentic is the second image?
A. Less authentic
B. About the same
C. More authentic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how authentic is the second image?\nA. Less authentic\nB. About the same\nC. More authentic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5311,[Response]: B.<|endoftext|>, [Correct Ans]: More rich, , [Prog]: 322:  32%|▎| 323/1000 [03:3
[Running Accuracy]: 0.5294,[Response]: C.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 323:  32%|▎| 323/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how authentic is the second image?\nA. Less authentic\nB. About the same\nC. More authentic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most seriously affected by overexposure?
A. The background of the first image
B. The apple in the first image
C. The black and white wall of the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most seriously affected by overexposure?
A. The background of the first image
B. The apple in the first image
C. The black and white wall of the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most seriously affected by overexposure?\nA. The background of the first image\nB. The apple in the first image\nC. The black and white wall of the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5294,[Response]: C.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 323:  32%|▎| 324/1000 
[Running Accuracy]: 0.5278,[Response]: A.<|endoftext|>, [Correct Ans]: The black and white wall of the second image, , 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most seriously affected by overexposure?\nA. The background of the first image\nB. The apple in the first image\nC. The black and white wall of the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images not very clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images not very clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images not very clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5278,[Response]: A.<|endoftext|>, [Correct Ans]: The black and white wall of the second image, , 
[Running Accuracy]: 0.5292,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 325:  32%|▎| 325/1000 [03:39<08:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images not very clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by motion blur?
A. The doll in the second image
B. The ground in the second image
C. The bottle carried by the dog in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by motion blur?
A. The doll in the second image
B. The ground in the second image
C. The bottle carried by the dog in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by motion blur?\nA. The doll in the second image\nB. The ground in the second image\nC. The bottle carried by the dog in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5292,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 325:  33%|▎| 326/1000 [03:40<07:3
[Running Accuracy]: 0.5276,[Response]: A.<|endoftext|>, [Correct Ans]: The bottle carried by the dog in the first image
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by motion blur?\nA. The doll in the second image\nB. The ground in the second image\nC. The bottle carried by the dog in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the two images both of relatively high clarity?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the two images both of relatively high clarity?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the two images both of relatively high clarity?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5276,[Response]: A.<|endoftext|>, [Correct Ans]: The bottle carried by the dog in the first image
[Running Accuracy]: 0.5291,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 327:  33%|▎| 327/1000 [03:40<07:13
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the two images both of relatively high clarity?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the sharpening of the first image more severe than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the sharpening of the first image more severe than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the sharpening of the first image more severe than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5291,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 327:  33%|▎| 328/1000 [03:41<06:53
[Running Accuracy]: 0.5305,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 328:  33%|▎| 328/1000 [03:41<06:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the sharpening of the first image more severe than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the texture detail of the first image less rich than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the texture detail of the first image less rich than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the texture detail of the first image less rich than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5305,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 328:  33%|▎| 329/1000 [03:42<06:4
[Running Accuracy]: 0.5289,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 329:  33%|▎| 329/1000 [03:42<06:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the texture detail of the first image less rich than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by motion blur?
A. The ground in the second image
B. The person in the second image
C. The person in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by motion blur?
A. The ground in the second image
B. The person in the second image
C. The person in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by motion blur?\nA. The ground in the second image\nB. The person in the second image\nC. The person in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5289,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 329:  33%|▎| 330/1000 [03:42<06:3
[Running Accuracy]: 0.5273,[Response]: B.<|endoftext|>, [Correct Ans]: The person in the first image, , [Prog]: 330:  3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by motion blur?\nA. The ground in the second image\nB. The person in the second image\nC. The person in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part has the richest texture details?
A. The desktop in the first image
B. The puppet in the first image
C. The insect on the left side in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part has the richest texture details?
A. The desktop in the first image
B. The puppet in the first image
C. The insect on the left side in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the richest texture details?\nA. The desktop in the first image\nB. The puppet in the first image\nC. The insect on the left side in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5273,[Response]: B.<|endoftext|>, [Correct Ans]: The person in the first image, , [Prog]: 330:  3
[Running Accuracy]: 0.5257,[Response]: B.<|endoftext|>, [Correct Ans]: The insect on the left side in the second image,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part has the richest texture details?\nA. The desktop in the first image\nB. The puppet in the first image\nC. The insect on the left side in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5257,[Response]: B.<|endoftext|>, [Correct Ans]: The insect on the left side in the second image,
[Running Accuracy]: 0.5271,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 332:  33%|▎| 332/1000 [03:43<06:33
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part has the richest texture details?
A. The pavilion in the second image
B. The hull of the first image
C. The water surface in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part has the richest texture details?
A. The pavilion in the second image
B. The hull of the first image
C. The water surface in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the richest texture details?\nA. The pavilion in the second image\nB. The hull of the first image\nC. The water surface in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5271,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 332:  33%|▎| 333/1000 [03:44<07:24
[Running Accuracy]: 0.5255,[Response]: C.<|endoftext|>, [Correct Ans]: The hull of the first image, , [Prog]: 333:  33%
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part has the richest texture details?\nA. The pavilion in the second image\nB. The hull of the first image\nC. The water surface in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5255,[Response]: C.<|endoftext|>, [Correct Ans]: The hull of the first image, , [Prog]: 333:  33%
[Running Accuracy]: 0.5269,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 334:  33%|▎| 334/1000 [03:45<06:57
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below has the most severe overexposure?
A. The sky in the upper right corner of the second image
B. The buildings in the second image
C. The lake surface in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below has the most severe overexposure?
A. The sky in the upper right corner of the second image
B. The buildings in the second image
C. The lake surface in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below has the most severe overexposure?\nA. The sky in the upper right corner of the second image\nB. The buildings in the second image\nC. The lake surface in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5269,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 334:  34%|▎| 335/1000 [03:45<06:39
[Running Accuracy]: 0.5284,[Response]: A.<|endoftext|>, [Correct Ans]: The sky in the upper right corner of the second 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below has the most severe overexposure?\nA. The sky in the upper right corner of the second image\nB. The buildings in the second image\nC. The lake surface in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the realism of the second image compare to the first image?
A. Similar
B. More realistic
C. Less realistic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the realism of the second image compare to the first image?
A. Similar
B. More realistic
C. Less realistic
Answer with the option's letter from the given choices directly.

prompts: [["How does the realism of the second image compare to the first image?\nA. Similar\nB. More realistic\nC. Less realistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5284,[Response]: A.<|endoftext|>, [Correct Ans]: The sky in the upper right corner of the second 
[Running Accuracy]: 0.5268,[Response]: A.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 336:  34%|▎| 336/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the realism of the second image compare to the first image?\nA. Similar\nB. More realistic\nC. Less realistic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5268,[Response]: A.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 336:  34%|▎| 337/1000 
[Running Accuracy]: 0.5282,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 337:  34%|▎| 337/1000 [03:46<06:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images relatively clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images relatively clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images relatively clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5282,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 337:  34%|▎| 338/1000 [03:47<06:2
[Running Accuracy]: 0.5296,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 338:  34%|▎| 338/1000 [03:47<06:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images relatively clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the sharpness of the second image?
A. Sharper
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the sharpness of the second image?
A. Sharper
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the sharpness of the second image?\nA. Sharper\nB. Blurrier\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5296,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 338:  34%|▎| 339/1000 [03:48<06:1
[Running Accuracy]: 0.5310,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 339:  34%|▎| 339/1000 [03:48
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the sharpness of the second image?\nA. Sharper\nB. Blurrier\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by motion blur?
A. People in the first image
B. The car window in the first image
C. The sky in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by motion blur?
A. People in the first image
B. The car window in the first image
C. The sky in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by motion blur?\nA. People in the first image\nB. The car window in the first image\nC. The sky in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5310,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 339:  34%|▎| 340/1000 [03:48
[Running Accuracy]: 0.5294,[Response]: A.<|endoftext|>, [Correct Ans]: The car window in the first image, , [Prog]: 340
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by motion blur?\nA. People in the first image\nB. The car window in the first image\nC. The sky in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. About the same
B. Clearer
C. More blurry
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. About the same
B. Clearer
C. More blurry
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. About the same\nB. Clearer\nC. More blurry\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5294,[Response]: A.<|endoftext|>, [Correct Ans]: The car window in the first image, , [Prog]: 340
[Running Accuracy]: 0.5308,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 341:  34%|▎| 341/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. About the same\nB. Clearer\nC. More blurry\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the sharpness of the second image?
A. Sharper
B. About the same
C. Blurrier
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the sharpness of the second image?
A. Sharper
B. About the same
C. Blurrier
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the sharpness of the second image?\nA. Sharper\nB. About the same\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5308,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 341:  34%|▎| 342/1000 
[Running Accuracy]: 0.5322,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 342:  34%|▎| 342/1000 [03:49
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the sharpness of the second image?\nA. Sharper\nB. About the same\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part has the richest texture details?
A. The wall in the first image
B. The dog on the right side in the second image
C. The characters in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part has the richest texture details?
A. The wall in the first image
B. The dog on the right side in the second image
C. The characters in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the richest texture details?\nA. The wall in the first image\nB. The dog on the right side in the second image\nC. The characters in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5322,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 342:  34%|▎| 343/1000 [03:50
[Running Accuracy]: 0.5335,[Response]: B.<|endoftext|>, [Correct Ans]: The dog on the right side in the second image, ,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part has the richest texture details?\nA. The wall in the first image\nB. The dog on the right side in the second image\nC. The characters in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the sharpness of the second image?
A. More blurry
B. About the same
C. Sharper
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the sharpness of the second image?
A. More blurry
B. About the same
C. Sharper
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the sharpness of the second image?\nA. More blurry\nB. About the same\nC. Sharper\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5335,[Response]: B.<|endoftext|>, [Correct Ans]: The dog on the right side in the second image, ,
[Running Accuracy]: 0.5349,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 344:  34%|▎| 344/1000 [03
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the sharpness of the second image?\nA. More blurry\nB. About the same\nC. Sharper\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5349,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 344:  34%|▎| 345/1000 [03
[Running Accuracy]: 0.5333,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 345:  34%|▎| 345/1000 [03:51<06:42
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. About the same
B. More blurry
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. About the same
B. More blurry
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. About the same\nB. More blurry\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5333,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 345:  35%|▎| 346/1000 [03:52<06:31
[Running Accuracy]: 0.5347,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 346:  35%|▎| 346/1000 [03
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. About the same\nB. More blurry\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>In comparison to the first image, how would you rate the authenticity of the second image?
A. Less authentic
B. About the same
C. More authentic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:In comparison to the first image, how would you rate the authenticity of the second image?
A. Less authentic
B. About the same
C. More authentic
Answer with the option's letter from the given choices directly.

prompts: [["In comparison to the first image, how would you rate the authenticity of the second image?\nA. Less authentic\nB. About the same\nC. More authentic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5347,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 346:  35%|▎| 347/1000 [03
[Running Accuracy]: 0.5331,[Response]: C.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 347:  35%|▎| 347/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>In comparison to the first image, how would you rate the authenticity of the second image?\nA. Less authentic\nB. About the same\nC. More authentic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by motion blur?
A. the figures in the second image
B. the tabletop in the first image
C. the figures in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by motion blur?
A. the figures in the second image
B. the tabletop in the first image
C. the figures in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by motion blur?\nA. the figures in the second image\nB. the tabletop in the first image\nC. the figures in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5331,[Response]: C.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 347:  35%|▎| 348/1000 
[Running Accuracy]: 0.5345,[Response]: A.<|endoftext|>, [Correct Ans]: the figures in the second image, , [Prog]: 348: 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by motion blur?\nA. the figures in the second image\nB. the tabletop in the first image\nC. the figures in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images relatively unrealistic?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images relatively unrealistic?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images relatively unrealistic?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5345,[Response]: A.<|endoftext|>, [Correct Ans]: the figures in the second image, , [Prog]: 348: 
[Running Accuracy]: 0.5358,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 349:  35%|▎| 349/1000 [03:53<06:59
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images relatively unrealistic?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. About the same
B. More blurry
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. About the same
B. More blurry
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. About the same\nB. More blurry\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5358,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 349:  35%|▎| 350/1000 [03:54<06:40
[Running Accuracy]: 0.5343,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 350:  35%|▎| 350/1000 [03
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. About the same\nB. More blurry\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How is the illumination of the second image relative to the first image?
A. more sufficient
B. about the same
C. less sufficient
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How is the illumination of the second image relative to the first image?
A. more sufficient
B. about the same
C. less sufficient
Answer with the option's letter from the given choices directly.

prompts: [["How is the illumination of the second image relative to the first image?\nA. more sufficient\nB. about the same\nC. less sufficient\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5343,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 350:  35%|▎| 351/1000 [03
[Running Accuracy]: 0.5356,[Response]: C.<|endoftext|>, [Correct Ans]: less sufficient, , [Prog]: 351:  35%|▎| 351/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How is the illumination of the second image relative to the first image?\nA. more sufficient\nB. about the same\nC. less sufficient\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the richness of colors in the second image?
A. Less rich
B. About the same
C. Richer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the richness of colors in the second image?
A. Less rich
B. About the same
C. Richer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the richness of colors in the second image?\nA. Less rich\nB. About the same\nC. Richer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5356,[Response]: C.<|endoftext|>, [Correct Ans]: less sufficient, , [Prog]: 351:  35%|▎| 352/1000
[Running Accuracy]: 0.5369,[Response]: C.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 352:  35%|▎| 352/1000 [03:55<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the richness of colors in the second image?\nA. Less rich\nB. About the same\nC. Richer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the lighting in the second image?
A. Less sufficient
B. More sufficient
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the lighting in the second image?
A. Less sufficient
B. More sufficient
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the lighting in the second image?\nA. Less sufficient\nB. More sufficient\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5369,[Response]: C.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 352:  35%|▎| 353/1000 [03:56<0
[Running Accuracy]: 0.5382,[Response]: A.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 353:  35%|▎| 353/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the lighting in the second image?\nA. Less sufficient\nB. More sufficient\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the texture details of these two images both rich?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the texture details of these two images both rich?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the texture details of these two images both rich?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5382,[Response]: A.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 353:  35%|▎| 354/1000
[Running Accuracy]: 0.5395,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 354:  35%|▎| 354/1000 [03:56<06:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the texture details of these two images both rich?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very blurry?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very blurry?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very blurry?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5395,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 354:  36%|▎| 355/1000 [03:57<06:0
[Running Accuracy]: 0.5408,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 355:  36%|▎| 355/1000 [03:57<06:04
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very blurry?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most affected by motion blur?
A. The hot air balloon in the first image
B. The sky in the first image
C. The leaves in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most affected by motion blur?
A. The hot air balloon in the first image
B. The sky in the first image
C. The leaves in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most affected by motion blur?\nA. The hot air balloon in the first image\nB. The sky in the first image\nC. The leaves in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5408,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 355:  36%|▎| 356/1000 [03:57<05:59
[Running Accuracy]: 0.5393,[Response]: A.<|endoftext|>, [Correct Ans]: The leaves in the second image, , [Prog]: 356:  
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most affected by motion blur?\nA. The hot air balloon in the first image\nB. The sky in the first image\nC. The leaves in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5393,[Response]: A.<|endoftext|>, [Correct Ans]: The leaves in the second image, , [Prog]: 356:  
[Running Accuracy]: 0.5406,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 357:  36%|▎| 357/1000 [03:58<05:51
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below has the most severe overexposure?
A. sky in the second image
B. ground in the first image
C. penguins in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below has the most severe overexposure?
A. sky in the second image
B. ground in the first image
C. penguins in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below has the most severe overexposure?\nA. sky in the second image\nB. ground in the first image\nC. penguins in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5406,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 357:  36%|▎| 358/1000 [03:59<06:38
[Running Accuracy]: 0.5419,[Response]: A.<|endoftext|>, [Correct Ans]: sky in the second image, , [Prog]: 358:  36%|▎| 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below has the most severe overexposure?\nA. sky in the second image\nB. ground in the first image\nC. penguins in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. Flower center of the first image
B. Background of the first image
C. Ground of the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. Flower center of the first image
B. Background of the first image
C. Ground of the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. Flower center of the first image\nB. Background of the first image\nC. Ground of the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5419,[Response]: A.<|endoftext|>, [Correct Ans]: sky in the second image, , [Prog]: 358:  36%|▎| 
[Running Accuracy]: 0.5404,[Response]: A.<|endoftext|>, [Correct Ans]: Ground of the second image, , [Prog]: 359:  36%|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. Flower center of the first image\nB. Background of the first image\nC. Ground of the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by motion blur?
A. The rider in the second image
B. The track in the first image
C. The train in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by motion blur?
A. The rider in the second image
B. The track in the first image
C. The train in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by motion blur?\nA. The rider in the second image\nB. The track in the first image\nC. The train in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.6406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5404,[Response]: A.<|endoftext|>, [Correct Ans]: Ground of the second image, , [Prog]: 359:  36%|
[Running Accuracy]: 0.5417,[Response]: A.<|endoftext|>, [Correct Ans]: The rider in the second image, , [Prog]: 360:  3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by motion blur?\nA. The rider in the second image\nB. The track in the first image\nC. The train in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how would you rate the authenticity of the second image?
A. Less authentic
B. More authentic
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how would you rate the authenticity of the second image?
A. Less authentic
B. More authentic
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how would you rate the authenticity of the second image?\nA. Less authentic\nB. More authentic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5417,[Response]: A.<|endoftext|>, [Correct Ans]: The rider in the second image, , [Prog]: 360:  3
[Running Accuracy]: 0.5429,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 361:  36%|▎| 361/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how would you rate the authenticity of the second image?\nA. Less authentic\nB. More authentic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. More blurry
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. More blurry
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5429,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 361:  36%|▎| 362/1000 
[Running Accuracy]: 0.5414,[Response]: C.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 362:  36%|▎| 362/1000 [04
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The sky in the first image
B. The train in the first image
C. The wall in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The sky in the first image
B. The train in the first image
C. The wall in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The sky in the first image\nB. The train in the first image\nC. The wall in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5414,[Response]: C.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 362:  36%|▎| 363/1000 [04
[Running Accuracy]: 0.5427,[Response]: A.<|endoftext|>, [Correct Ans]: The sky in the first image, , [Prog]: 363:  36%|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The sky in the first image\nB. The train in the first image\nC. The wall in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5427,[Response]: A.<|endoftext|>, [Correct Ans]: The sky in the first image, , [Prog]: 363:  36%|
[Running Accuracy]: 0.5440,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 364:  36%|▎| 364/1000 [04:03<06:22
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5440,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 364:  36%|▎| 365/1000 [04:03<06:11
[Running Accuracy]: 0.5425,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 365:  36%|▎| 365/1000 [04:03<06:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which kind of distortion issue does not exist in these two images?
A. noise
B. motion blur
C. overexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which kind of distortion issue does not exist in these two images?
A. noise
B. motion blur
C. overexposure
Answer with the option's letter from the given choices directly.

prompts: [["Which kind of distortion issue does not exist in these two images?\nA. noise\nB. motion blur\nC. overexposure\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5425,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 365:  37%|▎| 366/1000 [04:04<06:5
[Running Accuracy]: 0.5410,[Response]: C.<|endoftext|>, [Correct Ans]: noise, , [Prog]: 366:  37%|▎| 366/1000 [04:04<06
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which kind of distortion issue does not exist in these two images?\nA. noise\nB. motion blur\nC. overexposure\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the illumination sufficient in both of these images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the illumination sufficient in both of these images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the illumination sufficient in both of these images?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5410,[Response]: C.<|endoftext|>, [Correct Ans]: noise, , [Prog]: 366:  37%|▎| 367/1000 [04:04<06
[Running Accuracy]: 0.5422,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 367:  37%|▎| 367/1000 [04:04<06:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the illumination sufficient in both of these images?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most affected by motion blur?
A. The building in the first image
B. The person in the second image
C. The car in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most affected by motion blur?
A. The building in the first image
B. The person in the second image
C. The car in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most affected by motion blur?\nA. The building in the first image\nB. The person in the second image\nC. The car in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5422,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 367:  37%|▎| 368/1000 [04:05<06:1
[Running Accuracy]: 0.5435,[Response]: C.<|endoftext|>, [Correct Ans]: The car in the first image, , [Prog]: 368:  37%|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most affected by motion blur?\nA. The building in the first image\nB. The person in the second image\nC. The car in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most affected by out-of-focus?
A. The figure in the first image
B. The figure in the second image
C. The background in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most affected by out-of-focus?
A. The figure in the first image
B. The figure in the second image
C. The background in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most affected by out-of-focus?\nA. The figure in the first image\nB. The figure in the second image\nC. The background in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5435,[Response]: C.<|endoftext|>, [Correct Ans]: The car in the first image, , [Prog]: 368:  37%|
[Running Accuracy]: 0.5420,[Response]: A.<|endoftext|>, [Correct Ans]: The figure in the second image, , [Prog]: 369:  
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most affected by out-of-focus?\nA. The figure in the first image\nB. The figure in the second image\nC. The background in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5420,[Response]: A.<|endoftext|>, [Correct Ans]: The figure in the second image, , [Prog]: 369:  
[Running Accuracy]: 0.5405,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 370:  37%|▎| 370/1000 [04:06<06:00
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by motion blur?
A. Background of the first image
B. Person in the first image
C. Flower in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by motion blur?
A. Background of the first image
B. Person in the first image
C. Flower in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by motion blur?\nA. Background of the first image\nB. Person in the first image\nC. Flower in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5405,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 370:  37%|▎| 371/1000 [04:07<06:43
[Running Accuracy]: 0.5391,[Response]: B.<|endoftext|>, [Correct Ans]: Flower in the second image, , [Prog]: 371:  37%|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by motion blur?\nA. Background of the first image\nB. Person in the first image\nC. Flower in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images not very clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images not very clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images not very clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5391,[Response]: B.<|endoftext|>, [Correct Ans]: Flower in the second image, , [Prog]: 371:  37%|
[Running Accuracy]: 0.5376,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 372:  37%|▎| 372/1000 [04:07<06:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images not very clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the two images similar in clarity?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the two images similar in clarity?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the two images similar in clarity?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5376,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 372:  37%|▎| 373/1000 [04:08<06:0
[Running Accuracy]: 0.5362,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 373:  37%|▎| 373/1000 [04:08<06:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the two images similar in clarity?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by motion blur?
A. Ground in the first image
B. Dog in the first image
C. Person in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by motion blur?
A. Ground in the first image
B. Dog in the first image
C. Person in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by motion blur?\nA. Ground in the first image\nB. Dog in the first image\nC. Person in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5362,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 373:  37%|▎| 374/1000 [04:08<05:5
[Running Accuracy]: 0.5348,[Response]: C.<|endoftext|>, [Correct Ans]: Dog in the first image, , [Prog]: 374:  37%|▎| 3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by motion blur?\nA. Ground in the first image\nB. Dog in the first image\nC. Person in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by overexposure?
A. the top of the first image
B. bird in the second image
C. ground in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by overexposure?
A. the top of the first image
B. bird in the second image
C. ground in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by overexposure?\nA. the top of the first image\nB. bird in the second image\nC. ground in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5348,[Response]: C.<|endoftext|>, [Correct Ans]: Dog in the first image, , [Prog]: 374:  38%|▍| 3
[Running Accuracy]: 0.5360,[Response]: A.<|endoftext|>, [Correct Ans]: the top of the first image, , [Prog]: 375:  38%|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by overexposure?\nA. the top of the first image\nB. bird in the second image\nC. ground in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by overexposure?
A. the sky in the first image
B. the horse in the second image
C. the ground in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by overexposure?
A. the sky in the first image
B. the horse in the second image
C. the ground in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by overexposure?\nA. the sky in the first image\nB. the horse in the second image\nC. the ground in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5360,[Response]: A.<|endoftext|>, [Correct Ans]: the top of the first image, , [Prog]: 375:  38%|
[Running Accuracy]: 0.5372,[Response]: A.<|endoftext|>, [Correct Ans]: the sky in the first image, , [Prog]: 376:  38%|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by overexposure?\nA. the sky in the first image\nB. the horse in the second image\nC. the ground in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most affected by motion blur?
A. The ground in the first image
B. The person in the first image
C. The cat in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most affected by motion blur?
A. The ground in the first image
B. The person in the first image
C. The cat in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most affected by motion blur?\nA. The ground in the first image\nB. The person in the first image\nC. The cat in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5000], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5372,[Response]: A.<|endoftext|>, [Correct Ans]: the sky in the first image, , [Prog]: 376:  38%|
[Running Accuracy]: 0.5358,[Response]: B.<|endoftext|>, [Correct Ans]: The cat in the second image, , [Prog]: 377:  38%
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most affected by motion blur?\nA. The ground in the first image\nB. The person in the first image\nC. The cat in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by overexposure?
A. The figures in the second image
B. The outside of the upper left window in the first image
C. The background of the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by overexposure?
A. The figures in the second image
B. The outside of the upper left window in the first image
C. The background of the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by overexposure?\nA. The figures in the second image\nB. The outside of the upper left window in the first image\nC. The background of the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5358,[Response]: B.<|endoftext|>, [Correct Ans]: The cat in the second image, , [Prog]: 377:  38%
[Running Accuracy]: 0.5370,[Response]: B.<|endoftext|>, [Correct Ans]: The outside of the upper left window in the firs
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by overexposure?\nA. The figures in the second image\nB. The outside of the upper left window in the first image\nC. The background of the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by overexposure?
A. Sky in the first image
B. Person in the second image
C. Person in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by overexposure?
A. Sky in the first image
B. Person in the second image
C. Person in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by overexposure?\nA. Sky in the first image\nB. Person in the second image\nC. Person in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-29.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5370,[Response]: B.<|endoftext|>, [Correct Ans]: The outside of the upper left window in the firs
[Running Accuracy]: 0.5383,[Response]: A.<|endoftext|>, [Correct Ans]: Sky in the first image, , [Prog]: 379:  38%|▍| 3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by overexposure?\nA. Sky in the first image\nB. Person in the second image\nC. Person in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the texture detail of the first image richer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the texture detail of the first image richer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the texture detail of the first image richer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5383,[Response]: A.<|endoftext|>, [Correct Ans]: Sky in the first image, , [Prog]: 379:  38%|▍| 3
[Running Accuracy]: 0.5368,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 380:  38%|▍| 380/1000 [04:12<05:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the texture detail of the first image richer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very real?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very real?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very real?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5000], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5368,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 380:  38%|▍| 381/1000 [04:12<05:4
[Running Accuracy]: 0.5381,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 381:  38%|▍| 381/1000 [04:12<05:42
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very real?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image less vibrant than the color of the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image less vibrant than the color of the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image less vibrant than the color of the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5381,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 381:  38%|▍| 382/1000 [04:13<05:41
[Running Accuracy]: 0.5393,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 382:  38%|▍| 382/1000 [04:13<05:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image less vibrant than the color of the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the authenticity of the first image lower than that of the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the authenticity of the first image lower than that of the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the authenticity of the first image lower than that of the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-29.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5393,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 382:  38%|▍| 383/1000 [04:13<05:4
[Running Accuracy]: 0.5379,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 383:  38%|▍| 383/1000 [04:13<05:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the authenticity of the first image lower than that of the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image less rich than that of the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image less rich than that of the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image less rich than that of the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5379,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 383:  38%|▍| 384/1000 [04:14<06:0
[Running Accuracy]: 0.5365,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 384:  38%|▍| 384/1000 [04:14<06:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image less rich than that of the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by overexposure?
A. The child in the second image
B. The sky in the first image
C. The window in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by overexposure?
A. The child in the second image
B. The sky in the first image
C. The window in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by overexposure?\nA. The child in the second image\nB. The sky in the first image\nC. The window in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5365,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 384:  38%|▍| 385/1000 [04:15<06:0
[Running Accuracy]: 0.5377,[Response]: C.<|endoftext|>, [Correct Ans]: The window in the second image, , [Prog]: 385:  
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by overexposure?\nA. The child in the second image\nB. The sky in the first image\nC. The window in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most affected by motion blur?
A. Figures in the first image
B. Ground in the first image
C. Figures in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most affected by motion blur?
A. Figures in the first image
B. Ground in the first image
C. Figures in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most affected by motion blur?\nA. Figures in the first image\nB. Ground in the first image\nC. Figures in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5377,[Response]: C.<|endoftext|>, [Correct Ans]: The window in the second image, , [Prog]: 385:  
[Running Accuracy]: 0.5363,[Response]: A.<|endoftext|>, [Correct Ans]: Figures in the second image, , [Prog]: 386:  39%
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most affected by motion blur?\nA. Figures in the first image\nB. Ground in the first image\nC. Figures in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the authenticity of the first image lower than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the authenticity of the first image lower than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the authenticity of the first image lower than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.6562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5363,[Response]: A.<|endoftext|>, [Correct Ans]: Figures in the second image, , [Prog]: 386:  39%
[Running Accuracy]: 0.5375,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 387:  39%|▍| 387/1000 [04:16<05:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the authenticity of the first image lower than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the fidelity of the first image lower than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the fidelity of the first image lower than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the fidelity of the first image lower than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5375,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 387:  39%|▍| 388/1000 [04:16<05:4
[Running Accuracy]: 0.5361,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 388:  39%|▍| 388/1000 [04:16<05:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the fidelity of the first image lower than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part has the richest texture details?
A. The background of the second image
B. The character in the first image
C. The butterfly in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part has the richest texture details?
A. The background of the second image
B. The character in the first image
C. The butterfly in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the richest texture details?\nA. The background of the second image\nB. The character in the first image\nC. The butterfly in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5361,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 388:  39%|▍| 389/1000 [04:17<05:5
[Running Accuracy]: 0.5347,[Response]: B.<|endoftext|>, [Correct Ans]: The butterfly in the second image, , [Prog]: 389
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part has the richest texture details?\nA. The background of the second image\nB. The character in the first image\nC. The butterfly in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image much blurrier than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image much blurrier than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image much blurrier than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5347,[Response]: B.<|endoftext|>, [Correct Ans]: The butterfly in the second image, , [Prog]: 389
[Running Accuracy]: 0.5359,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 390:  39%|▍| 390/1000 [04:18<06:02
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image much blurrier than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below has been most affected by overexposure?
A. The dog in the second image
B. The middle part of the sky in the second image
C. The robot in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below has been most affected by overexposure?
A. The dog in the second image
B. The middle part of the sky in the second image
C. The robot in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below has been most affected by overexposure?\nA. The dog in the second image\nB. The middle part of the sky in the second image\nC. The robot in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5359,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 390:  39%|▍| 391/1000 [04:18<06:11
[Running Accuracy]: 0.5371,[Response]: B.<|endoftext|>, [Correct Ans]: The middle part of the sky in the second image, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below has been most affected by overexposure?\nA. The dog in the second image\nB. The middle part of the sky in the second image\nC. The robot in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5371,[Response]: B.<|endoftext|>, [Correct Ans]: The middle part of the sky in the second image, 
[Running Accuracy]: 0.5357,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 392:  39%|▍| 392/1000 [04:19<06:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5357,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 392:  39%|▍| 393/1000 [04:20<06:2
[Running Accuracy]: 0.5369,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 393:  39%|▍| 393/1000 [04:20<06:25
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part has the richest texture details?
A. Ground in the first image
B. Ground in the second image
C. Wooden barrel in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part has the richest texture details?
A. Ground in the first image
B. Ground in the second image
C. Wooden barrel in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the richest texture details?\nA. Ground in the first image\nB. Ground in the second image\nC. Wooden barrel in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-29.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5369,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 393:  39%|▍| 394/1000 [04:20<06:16
[Running Accuracy]: 0.5381,[Response]: C.<|endoftext|>, [Correct Ans]: Wooden barrel in the second image, , [Prog]: 394
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part has the richest texture details?\nA. Ground in the first image\nB. Ground in the second image\nC. Wooden barrel in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the realism of the first image higher than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the realism of the first image higher than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the realism of the first image higher than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5381,[Response]: C.<|endoftext|>, [Correct Ans]: Wooden barrel in the second image, , [Prog]: 394
[Running Accuracy]: 0.5367,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 395:  40%|▍| 395/1000 [04:21<06:09
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the realism of the first image higher than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by overexposure?
A. characters in the first image
B. characters in the second image
C. window in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by overexposure?
A. characters in the first image
B. characters in the second image
C. window in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by overexposure?\nA. characters in the first image\nB. characters in the second image\nC. window in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5367,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 395:  40%|▍| 396/1000 [04:21<06:01
[Running Accuracy]: 0.5379,[Response]: C.<|endoftext|>, [Correct Ans]: window in the first image, , [Prog]: 396:  40%|▍
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by overexposure?\nA. characters in the first image\nB. characters in the second image\nC. window in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5379,[Response]: C.<|endoftext|>, [Correct Ans]: window in the first image, , [Prog]: 396:  40%|▍
[Running Accuracy]: 0.5390,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 397:  40%|▍| 397/1000 [04:22<06:17
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by overexposure?
A. Characters in the first image
B. Top right corner of the second image
C. Ground in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by overexposure?
A. Characters in the first image
B. Top right corner of the second image
C. Ground in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by overexposure?\nA. Characters in the first image\nB. Top right corner of the second image\nC. Ground in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5390,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 397:  40%|▍| 398/1000 [04:23<06:33
[Running Accuracy]: 0.5402,[Response]: B.<|endoftext|>, [Correct Ans]: Top right corner of the second image, , [Prog]: 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by overexposure?\nA. Characters in the first image\nB. Top right corner of the second image\nC. Ground in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image richer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image richer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image richer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5402,[Response]: B.<|endoftext|>, [Correct Ans]: Top right corner of the second image, , [Prog]: 
[Running Accuracy]: 0.5388,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 399:  40%|▍| 399/1000 [04:24<07:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image richer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how does the realism of the second image compare?
A. Similar
B. More realistic
C. Less realistic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how does the realism of the second image compare?
A. Similar
B. More realistic
C. Less realistic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how does the realism of the second image compare?\nA. Similar\nB. More realistic\nC. Less realistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5388,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 399:  40%|▍| 400/1000 [04:24<07:2
[Running Accuracy]: 0.5375,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 400:  40%|▍| 400/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how does the realism of the second image compare?\nA. Similar\nB. More realistic\nC. Less realistic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Similar
B. Clearer
C. Less clear
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Similar
B. Clearer
C. Less clear
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Similar\nB. Clearer\nC. Less clear\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-29.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5375,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 400:  40%|▍| 401/1000 
[Running Accuracy]: 0.5387,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 401:  40%|▍| 401/1000 [04:25<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Similar\nB. Clearer\nC. Less clear\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how authentic is the second image?
A. Almost the same
B. More authentic
C. Less authentic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how authentic is the second image?
A. Almost the same
B. More authentic
C. Less authentic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how authentic is the second image?\nA. Almost the same\nB. More authentic\nC. Less authentic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5387,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 401:  40%|▍| 402/1000 [04:26<
[Running Accuracy]: 0.5373,[Response]: A.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 402:  40%|▍| 402/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how authentic is the second image?\nA. Almost the same\nB. More authentic\nC. Less authentic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images relatively true?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images relatively true?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images relatively true?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5373,[Response]: A.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 402:  40%|▍| 403/1000 
[Running Accuracy]: 0.5385,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 403:  40%|▍| 403/1000 [04:26<06:27
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images relatively true?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the authenticity of the second image?
A. Less authentic
B. About the same
C. More authentic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the authenticity of the second image?
A. Less authentic
B. About the same
C. More authentic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the authenticity of the second image?\nA. Less authentic\nB. About the same\nC. More authentic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5385,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 403:  40%|▍| 404/1000 [04:27<06:24
[Running Accuracy]: 0.5371,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 404:  40%|▍| 404/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the authenticity of the second image?\nA. Less authentic\nB. About the same\nC. More authentic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the lighting in the second image?
A. Similar
B. More sufficient
C. Less sufficient
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the lighting in the second image?
A. Similar
B. More sufficient
C. Less sufficient
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the lighting in the second image?\nA. Similar\nB. More sufficient\nC. Less sufficient\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5371,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 404:  40%|▍| 405/1000 
[Running Accuracy]: 0.5383,[Response]: C.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 405:  40%|▍| 405/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the lighting in the second image?\nA. Similar\nB. More sufficient\nC. Less sufficient\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The left side light in the second image
B. The sky in the first image
C. The building in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The left side light in the second image
B. The sky in the first image
C. The building in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The left side light in the second image\nB. The sky in the first image\nC. The building in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5383,[Response]: C.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 405:  41%|▍| 406/1000
[Running Accuracy]: 0.5394,[Response]: A.<|endoftext|>, [Correct Ans]: The left side light in the second image, , [Prog
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The left side light in the second image\nB. The sky in the first image\nC. The building in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part has the richest texture details?
A. Surface of the first image's lake
B. Clothing of the person in the second image
C. Sky in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part has the richest texture details?
A. Surface of the first image's lake
B. Clothing of the person in the second image
C. Sky in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the richest texture details?\nA. Surface of the first image's lake\nB. Clothing of the person in the second image\nC. Sky in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5394,[Response]: A.<|endoftext|>, [Correct Ans]: The left side light in the second image, , [Prog
[Running Accuracy]: 0.5405,[Response]: B.<|endoftext|>, [Correct Ans]: Clothing of the person in the second image, , [P
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part has the richest texture details?\nA. Surface of the first image's lake\nB. Clothing of the person in the second image\nC. Sky in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Similar
B. More blurry
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Similar
B. More blurry
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Similar\nB. More blurry\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5405,[Response]: B.<|endoftext|>, [Correct Ans]: Clothing of the person in the second image, , [P
[Running Accuracy]: 0.5417,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 408:  41%|▍| 408/1000 [04:30<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Similar\nB. More blurry\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how does the realism of the second image differ?
A. Less realistic
B. More realistic
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how does the realism of the second image differ?
A. Less realistic
B. More realistic
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how does the realism of the second image differ?\nA. Less realistic\nB. More realistic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5417,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 408:  41%|▍| 409/1000 [04:30<
[Running Accuracy]: 0.5428,[Response]: B.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 409:  41%|▍| 409/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how does the realism of the second image differ?\nA. Less realistic\nB. More realistic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most affected by the light halo and smear?
A. The sky in the second image
B. The light source in the second image
C. The sky in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most affected by the light halo and smear?
A. The sky in the second image
B. The light source in the second image
C. The sky in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most affected by the light halo and smear?\nA. The sky in the second image\nB. The light source in the second image\nC. The sky in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5428,[Response]: B.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 409:  41%|▍| 410/1000 
[Running Accuracy]: 0.5439,[Response]: B.<|endoftext|>, [Correct Ans]: The light source in the second image, , [Prog]: 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most affected by the light halo and smear?\nA. The sky in the second image\nB. The light source in the second image\nC. The sky in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part has the richest texture detail?
A. Statue in the first image
B. Character in the second image
C. Character in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part has the richest texture detail?
A. Statue in the first image
B. Character in the second image
C. Character in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the richest texture detail?\nA. Statue in the first image\nB. Character in the second image\nC. Character in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5439,[Response]: B.<|endoftext|>, [Correct Ans]: The light source in the second image, , [Prog]: 
[Running Accuracy]: 0.5450,[Response]: A.<|endoftext|>, [Correct Ans]: Statue in the first image, , [Prog]: 411:  41%|▍
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part has the richest texture detail?\nA. Statue in the first image\nB. Character in the second image\nC. Character in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5450,[Response]: A.<|endoftext|>, [Correct Ans]: Statue in the first image, , [Prog]: 411:  41%|▍
[Running Accuracy]: 0.5437,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 412:  41%|▍| 412/1000 [04:32<06:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the color vividness of the second image?
A. Similar
B. Less vivid
C. More vivid
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the color vividness of the second image?
A. Similar
B. Less vivid
C. More vivid
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the color vividness of the second image?\nA. Similar\nB. Less vivid\nC. More vivid\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5437,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 412:  41%|▍| 413/1000 [04:33<05:5
[Running Accuracy]: 0.5424,[Response]: B.<|endoftext|>, [Correct Ans]: More vivid, , [Prog]: 413:  41%|▍| 413/1000 [04:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the color vividness of the second image?\nA. Similar\nB. Less vivid\nC. More vivid\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The flowers in the first image
B. The sky in the second image
C. The wall in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The flowers in the first image
B. The sky in the second image
C. The wall in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The flowers in the first image\nB. The sky in the second image\nC. The wall in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5424,[Response]: B.<|endoftext|>, [Correct Ans]: More vivid, , [Prog]: 413:  41%|▍| 414/1000 [04:
[Running Accuracy]: 0.5435,[Response]: B.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 414:  41%
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The flowers in the first image\nB. The sky in the second image\nC. The wall in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how does the authenticity of the second image compare?
A. Less authentic
B. More authentic
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how does the authenticity of the second image compare?
A. Less authentic
B. More authentic
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how does the authenticity of the second image compare?\nA. Less authentic\nB. More authentic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5435,[Response]: B.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 414:  42%
[Running Accuracy]: 0.5446,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 415:  42%|▍| 415/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how does the authenticity of the second image compare?\nA. Less authentic\nB. More authentic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the composition of the first image better than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the composition of the first image better than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the composition of the first image better than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5446,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 415:  42%|▍| 416/1000 
[Running Accuracy]: 0.5433,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 416:  42%|▍| 416/1000 [04:35<07:10
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the composition of the first image better than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the richness of colors in the second image?
A. Similar
B. Less rich
C. More rich
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the richness of colors in the second image?
A. Similar
B. Less rich
C. More rich
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the richness of colors in the second image?\nA. Similar\nB. Less rich\nC. More rich\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5433,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 416:  42%|▍| 417/1000 [04:36<06:36
[Running Accuracy]: 0.5444,[Response]: C.<|endoftext|>, [Correct Ans]: More rich, , [Prog]: 417:  42%|▍| 417/1000 [04:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the richness of colors in the second image?\nA. Similar\nB. Less rich\nC. More rich\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5444,[Response]: C.<|endoftext|>, [Correct Ans]: More rich, , [Prog]: 417:  42%|▍| 418/1000 [04:3
[Running Accuracy]: 0.5431,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 418:  42%|▍| 418/1000 [04:36<06:08
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the authenticity of the second image?
A. More authentic
B. About the same
C. Less authentic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the authenticity of the second image?
A. More authentic
B. About the same
C. Less authentic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the authenticity of the second image?\nA. More authentic\nB. About the same\nC. Less authentic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5431,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 418:  42%|▍| 419/1000 [04:37<05:52
[Running Accuracy]: 0.5442,[Response]: A.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 419:  42%|▍| 419/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the authenticity of the second image?\nA. More authentic\nB. About the same\nC. Less authentic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by motion blur?
A. The figure in the first image
B. The wall in the first image
C. The figure in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by motion blur?
A. The figure in the first image
B. The wall in the first image
C. The figure in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by motion blur?\nA. The figure in the first image\nB. The wall in the first image\nC. The figure in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5442,[Response]: A.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 419:  42%|▍| 420/1000 
[Running Accuracy]: 0.5452,[Response]: A.<|endoftext|>, [Correct Ans]: The figure in the first image, , [Prog]: 420:  4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by motion blur?\nA. The figure in the first image\nB. The wall in the first image\nC. The figure in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most affected by motion blur?
A. The person in the first image
B. The telephone booth in the first image
C. The background in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most affected by motion blur?
A. The person in the first image
B. The telephone booth in the first image
C. The background in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most affected by motion blur?\nA. The person in the first image\nB. The telephone booth in the first image\nC. The background in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5452,[Response]: A.<|endoftext|>, [Correct Ans]: The figure in the first image, , [Prog]: 420:  4
[Running Accuracy]: 0.5463,[Response]: A.<|endoftext|>, [Correct Ans]: The person in the first image, , [Prog]: 421:  4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most affected by motion blur?\nA. The person in the first image\nB. The telephone booth in the first image\nC. The background in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by motion blur?
A. The moving person in the first image
B. The person in the second image
C. The background in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by motion blur?
A. The moving person in the first image
B. The person in the second image
C. The background in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by motion blur?\nA. The moving person in the first image\nB. The person in the second image\nC. The background in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5463,[Response]: A.<|endoftext|>, [Correct Ans]: The person in the first image, , [Prog]: 421:  4
[Running Accuracy]: 0.5474,[Response]: A.<|endoftext|>, [Correct Ans]: The moving person in the first image, , [Prog]: 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by motion blur?\nA. The moving person in the first image\nB. The person in the second image\nC. The background in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the illumination in the second image?
A. Similar
B. More sufficient
C. Less sufficient
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the illumination in the second image?
A. Similar
B. More sufficient
C. Less sufficient
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the illumination in the second image?\nA. Similar\nB. More sufficient\nC. Less sufficient\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5474,[Response]: A.<|endoftext|>, [Correct Ans]: The moving person in the first image, , [Prog]: 
[Running Accuracy]: 0.5485,[Response]: C.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 423:  42%|▍| 423/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the illumination in the second image?\nA. Similar\nB. More sufficient\nC. Less sufficient\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most affected by motion blur?
A. Sky in the second image
B. Building in the second image
C. Ground in the first image
D. Running person in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most affected by motion blur?
A. Sky in the second image
B. Building in the second image
C. Ground in the first image
D. Running person in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most affected by motion blur?\nA. Sky in the second image\nB. Building in the second image\nC. Ground in the first image\nD. Running person in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.5485,[Response]: C.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 423:  42%|▍| 424/1000
[Running Accuracy]: 0.5495,[Response]: D.<|endoftext|>, [Correct Ans]: Running person in the first image, , [Prog]: 424
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most affected by motion blur?\nA. Sky in the second image\nB. Building in the second image\nC. Ground in the first image\nD. Running person in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part has the richest texture details?
A. The monster's head in the second image
B. The ground in the first image
C. The character in the first image
D. The background in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part has the richest texture details?
A. The monster's head in the second image
B. The ground in the first image
C. The character in the first image
D. The background in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the richest texture details?\nA. The monster's head in the second image\nB. The ground in the first image\nC. The character in the first image\nD. The background in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5495,[Response]: D.<|endoftext|>, [Correct Ans]: Running person in the first image, , [Prog]: 424
[Running Accuracy]: 0.5506,[Response]: A.<|endoftext|>, [Correct Ans]: The monster's head in the second image, , [Prog]
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part has the richest texture details?\nA. The monster's head in the second image\nB. The ground in the first image\nC. The character in the first image\nD. The background in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the texture detail of the second image compare to the first image?
A. Richer
B. About the same
C. Less rich
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the texture detail of the second image compare to the first image?
A. Richer
B. About the same
C. Less rich
Answer with the option's letter from the given choices directly.

prompts: [["How does the texture detail of the second image compare to the first image?\nA. Richer\nB. About the same\nC. Less rich\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5506,[Response]: A.<|endoftext|>, [Correct Ans]: The monster's head in the second image, , [Prog]
[Running Accuracy]: 0.5493,[Response]: A.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 426:  43%|▍| 426/1000 [04:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the texture detail of the second image compare to the first image?\nA. Richer\nB. About the same\nC. Less rich\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the realism of the second image?
A. Less realistic
B. About the same
C. More realistic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the realism of the second image?
A. Less realistic
B. About the same
C. More realistic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the realism of the second image?\nA. Less realistic\nB. About the same\nC. More realistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5493,[Response]: A.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 426:  43%|▍| 427/1000 [04:4
[Running Accuracy]: 0.5504,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 427:  43%|▍| 427/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the realism of the second image?\nA. Less realistic\nB. About the same\nC. More realistic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by overexposure?
A. The floral wreath in the first image
B. The person's face in the first image
C. The distant light source in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by overexposure?
A. The floral wreath in the first image
B. The person's face in the first image
C. The distant light source in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by overexposure?\nA. The floral wreath in the first image\nB. The person's face in the first image\nC. The distant light source in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5504,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 427:  43%|▍| 428/1000 
[Running Accuracy]: 0.5514,[Response]: C.<|endoftext|>, [Correct Ans]: The distant light source in the second image, , 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by overexposure?\nA. The floral wreath in the first image\nB. The person's face in the first image\nC. The distant light source in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images relatively clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images relatively clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images relatively clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5514,[Response]: C.<|endoftext|>, [Correct Ans]: The distant light source in the second image, , 
[Running Accuracy]: 0.5524,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 429:  43%|▍| 429/1000 [04:43<05:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images relatively clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images free from overexposure issues?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images free from overexposure issues?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images free from overexposure issues?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5524,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 429:  43%|▍| 430/1000 [04:44<06:1
[Running Accuracy]: 0.5512,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 430:  43%|▍| 430/1000 [04:44<06:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images free from overexposure issues?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image is most severely affected by noise?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image is most severely affected by noise?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.

prompts: [["Which image is most severely affected by noise?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5512,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 430:  43%|▍| 431/1000 [04:44<05:5
[Running Accuracy]: 0.5522,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 431:  43%|▍| 431/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image is most severely affected by noise?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the two images both quite clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the two images both quite clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the two images both quite clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5522,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 431:  43%|▍| 432/1000 [0
[Running Accuracy]: 0.5532,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 432:  43%|▍| 432/1000 [04:45<05:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the two images both quite clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image blurrier than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image blurrier than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image blurrier than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5532,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 432:  43%|▍| 433/1000 [04:45<05:4
[Running Accuracy]: 0.5520,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 433:  43%|▍| 433/1000 [04:45<05:44
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image blurrier than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by out-of-focus?
A. Background of the first image
B. Stamen of the second image
C. Person in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by out-of-focus?
A. Background of the first image
B. Stamen of the second image
C. Person in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by out-of-focus?\nA. Background of the first image\nB. Stamen of the second image\nC. Person in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5520,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 433:  43%|▍| 434/1000 [04:46<05:55
[Running Accuracy]: 0.5530,[Response]: B.<|endoftext|>, [Correct Ans]: Stamen of the second image, , [Prog]: 434:  43%|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by out-of-focus?\nA. Background of the first image\nB. Stamen of the second image\nC. Person in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image does not have overexposure distortion issue?
A. First image
B. Second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image does not have overexposure distortion issue?
A. First image
B. Second image
Answer with the option's letter from the given choices directly.

prompts: [["Which image does not have overexposure distortion issue?\nA. First image\nB. Second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5530,[Response]: B.<|endoftext|>, [Correct Ans]: Stamen of the second image, , [Prog]: 434:  44%|
[Running Accuracy]: 0.5540,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 435:  44%|▍| 435/1000 [04
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image does not have overexposure distortion issue?\nA. First image\nB. Second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What kind of distortion did not appear in these two images?
A. Out of focus
B. Noise
C. Overexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What kind of distortion did not appear in these two images?
A. Out of focus
B. Noise
C. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion did not appear in these two images?\nA. Out of focus\nB. Noise\nC. Overexposure\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5540,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 435:  44%|▍| 436/1000 [04
[Running Accuracy]: 0.5550,[Response]: B.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 436:  44%|▍| 436/1000 [04:47<05
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What kind of distortion did not appear in these two images?\nA. Out of focus\nB. Noise\nC. Overexposure\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images blurry?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images blurry?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images blurry?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5550,[Response]: B.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 436:  44%|▍| 437/1000 [04:48<05
[Running Accuracy]: 0.5561,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 437:  44%|▍| 437/1000 [04:48<05:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images blurry?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5561,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 437:  44%|▍| 438/1000 [04:49<06:1
[Running Accuracy]: 0.5548,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 438:  44%|▍| 438/1000 [04:49<06:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image less clear than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image less clear than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image less clear than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5548,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 438:  44%|▍| 439/1000 [04:49<06:0
[Running Accuracy]: 0.5535,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 439:  44%|▍| 439/1000 [04:49<06:00
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image less clear than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image below has not been overexposed?
A. The second image
B. The first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image below has not been overexposed?
A. The second image
B. The first image
Answer with the option's letter from the given choices directly.

prompts: [["Which image below has not been overexposed?\nA. The second image\nB. The first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5535,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 439:  44%|▍| 440/1000 [04:50<05:43
[Running Accuracy]: 0.5523,[Response]: A.<|endoftext|>, [Correct Ans]: The first image, , [Prog]: 440:  44%|▍| 440/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image below has not been overexposed?\nA. The second image\nB. The first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5523,[Response]: A.<|endoftext|>, [Correct Ans]: The first image, , [Prog]: 440:  44%|▍| 441/1000
[Running Accuracy]: 0.5533,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 441:  44%|▍| 441/1000 [04:50<05:37
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5533,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 441:  44%|▍| 442/1000 [04:51<05:32
[Running Accuracy]: 0.5543,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 442:  44%|▍| 442/1000 [04:51<05:32
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. More blurry
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. More blurry
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5543,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 442:  44%|▍| 443/1000 [04:52<05:56
[Running Accuracy]: 0.5553,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 443:  44%|▍| 443/1000 [04:52<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by motion blur?
A. The table of the first T-shirt in the image
B. The racer in the second image
C. The background of the first image
D. The background trees in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by motion blur?
A. The table of the first T-shirt in the image
B. The racer in the second image
C. The background of the first image
D. The background trees in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by motion blur?\nA. The table of the first T-shirt in the image\nB. The racer in the second image\nC. The background of the first image\nD. The background trees in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5553,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 443:  44%|▍| 444/1000 [04:53<
[Running Accuracy]: 0.5541,[Response]: B.<|endoftext|>, [Correct Ans]: The background trees in the second image, , [Pro
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by motion blur?\nA. The table of the first T-shirt in the image\nB. The racer in the second image\nC. The background of the first image\nD. The background trees in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the color vividness of the second image?
A. more vivid
B. about the same
C. less vivid
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the color vividness of the second image?
A. more vivid
B. about the same
C. less vivid
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the color vividness of the second image?\nA. more vivid\nB. about the same\nC. less vivid\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5541,[Response]: B.<|endoftext|>, [Correct Ans]: The background trees in the second image, , [Pro
[Running Accuracy]: 0.5528,[Response]: C.<|endoftext|>, [Correct Ans]: more vivid, , [Prog]: 445:  44%|▍| 445/1000 [04:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the color vividness of the second image?\nA. more vivid\nB. about the same\nC. less vivid\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5528,[Response]: C.<|endoftext|>, [Correct Ans]: more vivid, , [Prog]: 445:  45%|▍| 446/1000 [04:
[Running Accuracy]: 0.5538,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 446:  45%|▍| 446/1000 [04:54<07:24
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is the clearest?
A. Computer screen in the first image
B. Bamboo on the left side of the second image
C. Text in the center of the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is the clearest?
A. Computer screen in the first image
B. Bamboo on the left side of the second image
C. Text in the center of the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is the clearest?\nA. Computer screen in the first image\nB. Bamboo on the left side of the second image\nC. Text in the center of the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5538,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 446:  45%|▍| 447/1000 [04:55<08:05
[Running Accuracy]: 0.5548,[Response]: C.<|endoftext|>, [Correct Ans]: Text in the center of the first image, , [Prog]:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is the clearest?\nA. Computer screen in the first image\nB. Bamboo on the left side of the second image\nC. Text in the center of the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5548,[Response]: C.<|endoftext|>, [Correct Ans]: Text in the center of the first image, , [Prog]:
[Running Accuracy]: 0.5558,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 448:  45%|▍| 448/1000 [04:57<08:52
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images relatively realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images relatively realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images relatively realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5558,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 448:  45%|▍| 449/1000 [04:57<07:54
[Running Accuracy]: 0.5568,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 449:  45%|▍| 449/1000 [04:57<07:54
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images relatively realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images relatively blurry?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images relatively blurry?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images relatively blurry?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5568,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 449:  45%|▍| 450/1000 [04:58<06:59
[Running Accuracy]: 0.5578,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 450:  45%|▍| 450/1000 [04:58<06:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images relatively blurry?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. Sky in the first image
B. People in the first image
C. Background in the second image
D. People in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. Sky in the first image
B. People in the first image
C. Background in the second image
D. People in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. Sky in the first image\nB. People in the first image\nC. Background in the second image\nD. People in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5578,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 450:  45%|▍| 451/1000 [04:59<07:1
[Running Accuracy]: 0.5588,[Response]: A.<|endoftext|>, [Correct Ans]: Sky in the first image, , [Prog]: 451:  45%|▍| 4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. Sky in the first image\nB. People in the first image\nC. Background in the second image\nD. People in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which of the following images is most affected by motion blur?
A. First image
B. Second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which of the following images is most affected by motion blur?
A. First image
B. Second image
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following images is most affected by motion blur?\nA. First image\nB. Second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.6875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5588,[Response]: A.<|endoftext|>, [Correct Ans]: Sky in the first image, , [Prog]: 451:  45%|▍| 4
[Running Accuracy]: 0.5597,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 452:  45%|▍| 452/1000 [04
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which of the following images is most affected by motion blur?\nA. First image\nB. Second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the sharpness of the second image higher than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the sharpness of the second image higher than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the sharpness of the second image higher than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5597,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 452:  45%|▍| 453/1000 [05
[Running Accuracy]: 0.5585,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 453:  45%|▍| 453/1000 [05:00<06:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the sharpness of the second image higher than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. Audience in the second image
B. Ground in the first image
C. Background light source in the second image
D. Sky in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. Audience in the second image
B. Ground in the first image
C. Background light source in the second image
D. Sky in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. Audience in the second image\nB. Ground in the first image\nC. Background light source in the second image\nD. Sky in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5585,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 453:  45%|▍| 454/1000 [05:01<06:2
[Running Accuracy]: 0.5595,[Response]: C.<|endoftext|>, [Correct Ans]: Background light source in the second image, , [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. Audience in the second image\nB. Ground in the first image\nC. Background light source in the second image\nD. Sky in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how does the color richness of the first image?
A. Less rich
B. About the same
C. More rich
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how does the color richness of the first image?
A. Less rich
B. About the same
C. More rich
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how does the color richness of the first image?\nA. Less rich\nB. About the same\nC. More rich\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5595,[Response]: C.<|endoftext|>, [Correct Ans]: Background light source in the second image, , [
[Running Accuracy]: 0.5582,[Response]: C.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 455:  46%|▍| 455/1000 [05:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how does the color richness of the first image?\nA. Less rich\nB. About the same\nC. More rich\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are there noise issues in both of these images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are there noise issues in both of these images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are there noise issues in both of these images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5582,[Response]: C.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 455:  46%|▍| 456/1000 [05:0
[Running Accuracy]: 0.5592,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 456:  46%|▍| 456/1000 [05:02<06:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are there noise issues in both of these images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the sharpness of the first image lower than that of the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the sharpness of the first image lower than that of the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the sharpness of the first image lower than that of the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5592,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 456:  46%|▍| 457/1000 [05:03<06:0
[Running Accuracy]: 0.5602,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 457:  46%|▍| 457/1000 [05:03<06:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the sharpness of the first image lower than that of the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5602,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 457:  46%|▍| 458/1000 [05:03<05:5
[Running Accuracy]: 0.5611,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 458:  46%|▍| 458/1000 [05:03<05:57
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. More blurry
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. More blurry
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5611,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 458:  46%|▍| 459/1000 [05:04<05:49
[Running Accuracy]: 0.5621,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 459:  46%|▍| 459/1000 [05
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by motion blur?
A. Background of the second image
B. Background of the first image
C. Subject in the first image
D. Subject in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by motion blur?
A. Background of the second image
B. Background of the first image
C. Subject in the first image
D. Subject in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by motion blur?\nA. Background of the second image\nB. Background of the first image\nC. Subject in the first image\nD. Subject in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5000], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5621,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 459:  46%|▍| 460/1000 [05
[Running Accuracy]: 0.5609,[Response]: C.<|endoftext|>, [Correct Ans]: Background of the second image, , [Prog]: 460:  
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by motion blur?\nA. Background of the second image\nB. Background of the first image\nC. Subject in the first image\nD. Subject in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images overexposed?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images overexposed?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images overexposed?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5609,[Response]: C.<|endoftext|>, [Correct Ans]: Background of the second image, , [Prog]: 460:  
[Running Accuracy]: 0.5618,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 461:  46%|▍| 461/1000 [05:05<05:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images overexposed?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the sharpness of the second image compare to the first image?
A. About the same
B. More blurry
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the sharpness of the second image compare to the first image?
A. About the same
B. More blurry
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["How does the sharpness of the second image compare to the first image?\nA. About the same\nB. More blurry\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5618,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 461:  46%|▍| 462/1000 [05:06<06:1
[Running Accuracy]: 0.5628,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 462:  46%|▍| 462/1000 [05
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the sharpness of the second image compare to the first image?\nA. About the same\nB. More blurry\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images overexposed?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images overexposed?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images overexposed?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5628,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 462:  46%|▍| 463/1000 [05
[Running Accuracy]: 0.5616,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 463:  46%|▍| 463/1000 [05:06<05:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images overexposed?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. More blurry
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. More blurry
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5616,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 463:  46%|▍| 464/1000 [05:07<05:3
[Running Accuracy]: 0.5603,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 464:  46%|▍| 464/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the composition of the second image?
A. worse
B. better
C. similar
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the composition of the second image?
A. worse
B. better
C. similar
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the composition of the second image?\nA. worse\nB. better\nC. similar\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5603,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 464:  46%|▍| 465/1000 
[Running Accuracy]: 0.5613,[Response]: B.<|endoftext|>, [Correct Ans]: better, , [Prog]: 465:  46%|▍| 465/1000 [05:08<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the composition of the second image?\nA. worse\nB. better\nC. similar\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the clarity of the second image compare to the first image?
A. Similar
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the clarity of the second image compare to the first image?
A. Similar
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.

prompts: [["How does the clarity of the second image compare to the first image?\nA. Similar\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5613,[Response]: B.<|endoftext|>, [Correct Ans]: better, , [Prog]: 465:  47%|▍| 466/1000 [05:09<0
[Running Accuracy]: 0.5622,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 466:  47%|▍| 466/1000 [05:09
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the clarity of the second image compare to the first image?\nA. Similar\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What kind of distortion issue does not exist in these two images?
A. Noise
B. Overexposure
C. Blur
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What kind of distortion issue does not exist in these two images?
A. Noise
B. Overexposure
C. Blur
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion issue does not exist in these two images?\nA. Noise\nB. Overexposure\nC. Blur\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5622,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 466:  47%|▍| 467/1000 [05:09
[Running Accuracy]: 0.5610,[Response]: B.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 467:  47%|▍| 467/1000 [05:09<06
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What kind of distortion issue does not exist in these two images?\nA. Noise\nB. Overexposure\nC. Blur\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the color vividness of the second image?
A. More vivid
B. Less vivid
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the color vividness of the second image?
A. More vivid
B. Less vivid
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the color vividness of the second image?\nA. More vivid\nB. Less vivid\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5610,[Response]: B.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 467:  47%|▍| 468/1000 [05:10<06
[Running Accuracy]: 0.5620,[Response]: A.<|endoftext|>, [Correct Ans]: More vivid, , [Prog]: 468:  47%|▍| 468/1000 [05:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the color vividness of the second image?\nA. More vivid\nB. Less vivid\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How is the texture detail of the second image compared to the first image?
A. More abundant
B. About the same
C. Less abundant
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How is the texture detail of the second image compared to the first image?
A. More abundant
B. About the same
C. Less abundant
Answer with the option's letter from the given choices directly.

prompts: [["How is the texture detail of the second image compared to the first image?\nA. More abundant\nB. About the same\nC. Less abundant\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5620,[Response]: A.<|endoftext|>, [Correct Ans]: More vivid, , [Prog]: 468:  47%|▍| 469/1000 [05:
[Running Accuracy]: 0.5629,[Response]: A.<|endoftext|>, [Correct Ans]: More abundant, , [Prog]: 469:  47%|▍| 469/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How is the texture detail of the second image compared to the first image?\nA. More abundant\nB. About the same\nC. Less abundant\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5629,[Response]: A.<|endoftext|>, [Correct Ans]: More abundant, , [Prog]: 469:  47%|▍| 470/1000 [
[Running Accuracy]: 0.5617,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 470:  47%|▍| 470/1000 [05:11<05:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are there no distortion issues with these two images?
A. noise
B. overexposure
C. underexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are there no distortion issues with these two images?
A. noise
B. overexposure
C. underexposure
Answer with the option's letter from the given choices directly.

prompts: [["Are there no distortion issues with these two images?\nA. noise\nB. overexposure\nC. underexposure\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5617,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 470:  47%|▍| 471/1000 [05:12<05:2
[Running Accuracy]: 0.5605,[Response]: B.<|endoftext|>, [Correct Ans]: underexposure, , [Prog]: 471:  47%|▍| 471/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are there no distortion issues with these two images?\nA. noise\nB. overexposure\nC. underexposure\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Similar
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Similar
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Similar\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5605,[Response]: B.<|endoftext|>, [Correct Ans]: underexposure, , [Prog]: 471:  47%|▍| 472/1000 [
[Running Accuracy]: 0.5593,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 472:  47%|▍| 472/1000 [05:12<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Similar\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image is more seriously affected by noise problems?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image is more seriously affected by noise problems?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.

prompts: [["Which image is more seriously affected by noise problems?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5593,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 472:  47%|▍| 473/1000 [05:13<
[Running Accuracy]: 0.5581,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 473:  47%|▍| 473/1000 [05
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image is more seriously affected by noise problems?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the overexposure issue in the second image more severe than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the overexposure issue in the second image more severe than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the overexposure issue in the second image more severe than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-29.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5581,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 473:  47%|▍| 474/1000 [05
[Running Accuracy]: 0.5591,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 474:  47%|▍| 474/1000 [05:13<05:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the overexposure issue in the second image more severe than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. About the same
B. Less clear
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. About the same
B. Less clear
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. About the same\nB. Less clear\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5591,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 474:  48%|▍| 475/1000 [05:14<06:1
[Running Accuracy]: 0.5600,[Response]: B.<|endoftext|>, [Correct Ans]: Less clear, , [Prog]: 475:  48%|▍| 475/1000 [05:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. About the same\nB. Less clear\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Similar
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Similar
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Similar\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5600,[Response]: B.<|endoftext|>, [Correct Ans]: Less clear, , [Prog]: 475:  48%|▍| 476/1000 [05:
[Running Accuracy]: 0.5609,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 476:  48%|▍| 476/1000 [05:15
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Similar\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. About the same
B. Blurrier
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. About the same
B. Blurrier
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. About the same\nB. Blurrier\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5609,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 476:  48%|▍| 477/1000 [05:16
[Running Accuracy]: 0.5597,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 477:  48%|▍| 477/1000 [05:16<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. About the same\nB. Blurrier\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images relatively realistic?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images relatively realistic?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images relatively realistic?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5597,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 477:  48%|▍| 478/1000 [05:16<
[Running Accuracy]: 0.5607,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 478:  48%|▍| 478/1000 [05:16<05:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images relatively realistic?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how clear is the second image?
A. Clearer
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how clear is the second image?
A. Clearer
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how clear is the second image?\nA. Clearer\nB. Blurrier\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5607,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 478:  48%|▍| 479/1000 [05:17<05:1
[Running Accuracy]: 0.5616,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 479:  48%|▍| 479/1000 [05:17
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how clear is the second image?\nA. Clearer\nB. Blurrier\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the color richness of the second image?
A. Similar
B. Less rich
C. Richer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the color richness of the second image?
A. Similar
B. Less rich
C. Richer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the color richness of the second image?\nA. Similar\nB. Less rich\nC. Richer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5616,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 479:  48%|▍| 480/1000 [05:17
[Running Accuracy]: 0.5604,[Response]: C.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 480:  48%|▍| 480/1000 [05:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the color richness of the second image?\nA. Similar\nB. Less rich\nC. Richer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image is more severely affected by noise?
A. First image
B. Second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image is more severely affected by noise?
A. First image
B. Second image
Answer with the option's letter from the given choices directly.

prompts: [["Which image is more severely affected by noise?\nA. First image\nB. Second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5604,[Response]: C.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 480:  48%|▍| 481/1000 [05:1
[Running Accuracy]: 0.5613,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 481:  48%|▍| 481/1000 [05
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image is more severely affected by noise?\nA. First image\nB. Second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5613,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 481:  48%|▍| 482/1000 [05
[Running Accuracy]: 0.5602,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 482:  48%|▍| 482/1000 [05:19<05:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how would you rate the realism of the second image?
A. Less realistic
B. More realistic
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how would you rate the realism of the second image?
A. Less realistic
B. More realistic
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how would you rate the realism of the second image?\nA. Less realistic\nB. More realistic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5602,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 482:  48%|▍| 483/1000 [05:19<05:3
[Running Accuracy]: 0.5590,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 483:  48%|▍| 483/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how would you rate the realism of the second image?\nA. Less realistic\nB. More realistic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Less clear
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Less clear
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Less clear\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5590,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 483:  48%|▍| 484/1000 
[Running Accuracy]: 0.5579,[Response]: A.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 484:  48%|▍| 484/1000 [05:20<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Less clear\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Relative to the first image, how is the clarity of the second image?
A. Less clear
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Relative to the first image, how is the clarity of the second image?
A. Less clear
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Relative to the first image, how is the clarity of the second image?\nA. Less clear\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5579,[Response]: A.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 484:  48%|▍| 485/1000 [05:21<
[Running Accuracy]: 0.5567,[Response]: A.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 485:  48%|▍| 485/1000 [05:21<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Relative to the first image, how is the clarity of the second image?\nA. Less clear\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5567,[Response]: A.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 485:  49%|▍| 486/1000 [05:21<
[Running Accuracy]: 0.5576,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 486:  49%|▍| 486/1000 [05:22<05:50
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the sharpness of the second image?
A. Sharper
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the sharpness of the second image?
A. Sharper
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the sharpness of the second image?\nA. Sharper\nB. Blurrier\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5576,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 486:  49%|▍| 487/1000 [05:22<05:31
[Running Accuracy]: 0.5585,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 487:  49%|▍| 487/1000 [05:22
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the sharpness of the second image?\nA. Sharper\nB. Blurrier\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which of the following images has a more serious overexposure issue?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which of the following images has a more serious overexposure issue?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following images has a more serious overexposure issue?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5585,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 487:  49%|▍| 488/1000 [05:23
[Running Accuracy]: 0.5594,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 488:  49%|▍| 488/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which of the following images has a more serious overexposure issue?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by snowflakes?
A. The ground in the second image
B. The waves in the first image
C. The plants in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by snowflakes?
A. The ground in the second image
B. The waves in the first image
C. The plants in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by snowflakes?\nA. The ground in the second image\nB. The waves in the first image\nC. The plants in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5594,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 488:  49%|▍| 489/1000 [0
[Running Accuracy]: 0.5583,[Response]: A.<|endoftext|>, [Correct Ans]: The waves in the first image, , [Prog]: 489:  49
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by snowflakes?\nA. The ground in the second image\nB. The waves in the first image\nC. The plants in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the illumination of the second image?
A. similar
B. more insufficient
C. more sufficient
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the illumination of the second image?
A. similar
B. more insufficient
C. more sufficient
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the illumination of the second image?\nA. similar\nB. more insufficient\nC. more sufficient\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5583,[Response]: A.<|endoftext|>, [Correct Ans]: The waves in the first image, , [Prog]: 489:  49
[Running Accuracy]: 0.5592,[Response]: B.<|endoftext|>, [Correct Ans]: more insufficient, , [Prog]: 490:  49%|▍| 490/10
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the illumination of the second image?\nA. similar\nB. more insufficient\nC. more sufficient\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the color richness of the second image?
A. More monotonous
B. About the same
C. More rich
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the color richness of the second image?
A. More monotonous
B. About the same
C. More rich
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the color richness of the second image?\nA. More monotonous\nB. About the same\nC. More rich\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5592,[Response]: B.<|endoftext|>, [Correct Ans]: more insufficient, , [Prog]: 490:  49%|▍| 491/10
[Running Accuracy]: 0.5601,[Response]: A.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 491:  49%|▍| 491/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the color richness of the second image?\nA. More monotonous\nB. About the same\nC. More rich\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below has a serious overexposure issue?
A. The painting in the second image
B. The background of the first image
C. The umbrella in the first image
D. The light source area in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below has a serious overexposure issue?
A. The painting in the second image
B. The background of the first image
C. The umbrella in the first image
D. The light source area in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below has a serious overexposure issue?\nA. The painting in the second image\nB. The background of the first image\nC. The umbrella in the first image\nD. The light source area in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.5601,[Response]: A.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 491:  49%|▍| 492/1000
[Running Accuracy]: 0.5610,[Response]: D.<|endoftext|>, [Correct Ans]: The light source area in the second image, , [Pr
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below has a serious overexposure issue?\nA. The painting in the second image\nB. The background of the first image\nC. The umbrella in the first image\nD. The light source area in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the sharpness of the second image?
A. Blurrier
B. Sharper
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the sharpness of the second image?
A. Blurrier
B. Sharper
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the sharpness of the second image?\nA. Blurrier\nB. Sharper\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5610,[Response]: D.<|endoftext|>, [Correct Ans]: The light source area in the second image, , [Pr
[Running Accuracy]: 0.5619,[Response]: A.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 493:  49%|▍| 493/1000 [05:27
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the sharpness of the second image?\nA. Blurrier\nB. Sharper\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by snowflakes?
A. Ground in the first image
B. Ground in the second image
C. Animal in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by snowflakes?
A. Ground in the first image
B. Ground in the second image
C. Animal in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by snowflakes?\nA. Ground in the first image\nB. Ground in the second image\nC. Animal in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5619,[Response]: A.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 493:  49%|▍| 494/1000 [05:27
[Running Accuracy]: 0.5607,[Response]: A.<|endoftext|>, [Correct Ans]: Ground in the second image, , [Prog]: 494:  49%|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by snowflakes?\nA. Ground in the first image\nB. Ground in the second image\nC. Animal in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the composition of the second image?
A. Similar
B. Better
C. Worse
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the composition of the second image?
A. Similar
B. Better
C. Worse
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the composition of the second image?\nA. Similar\nB. Better\nC. Worse\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5607,[Response]: A.<|endoftext|>, [Correct Ans]: Ground in the second image, , [Prog]: 494:  50%|
[Running Accuracy]: 0.5596,[Response]: C.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 495:  50%|▍| 495/1000 [05:28<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the composition of the second image?\nA. Similar\nB. Better\nC. Worse\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the lighting in the second image?
A. more sufficient
B. less sufficient
C. similar
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the lighting in the second image?
A. more sufficient
B. less sufficient
C. similar
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the lighting in the second image?\nA. more sufficient\nB. less sufficient\nC. similar\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5596,[Response]: C.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 495:  50%|▍| 496/1000 [05:28<0
[Running Accuracy]: 0.5605,[Response]: A.<|endoftext|>, [Correct Ans]: more sufficient, , [Prog]: 496:  50%|▍| 496/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the lighting in the second image?\nA. more sufficient\nB. less sufficient\nC. similar\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image below has a serious overexposure problem?
A. First image
B. Second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image below has a serious overexposure problem?
A. First image
B. Second image
Answer with the option's letter from the given choices directly.

prompts: [["Which image below has a serious overexposure problem?\nA. First image\nB. Second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5605,[Response]: A.<|endoftext|>, [Correct Ans]: more sufficient, , [Prog]: 496:  50%|▍| 497/1000
[Running Accuracy]: 0.5614,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 497:  50%|▍| 497/1000 [05
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image below has a serious overexposure problem?\nA. First image\nB. Second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the sharpness of the second image?
A. Sharper
B. About the same
C. Blurrier
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the sharpness of the second image?
A. Sharper
B. About the same
C. Blurrier
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the sharpness of the second image?\nA. Sharper\nB. About the same\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5614,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 497:  50%|▍| 498/1000 [05
[Running Accuracy]: 0.5602,[Response]: C.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 498:  50%|▍| 498/1000 [05:30<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the sharpness of the second image?\nA. Sharper\nB. About the same\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how does the realism of the second image hold up?
A. Similar
B. Less realistic
C. More realistic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how does the realism of the second image hold up?
A. Similar
B. Less realistic
C. More realistic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how does the realism of the second image hold up?\nA. Similar\nB. Less realistic\nC. More realistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5602,[Response]: C.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 498:  50%|▍| 499/1000 [05:30<
[Running Accuracy]: 0.5611,[Response]: B.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 499:  50%|▍| 499/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how does the realism of the second image hold up?\nA. Similar\nB. Less realistic\nC. More realistic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Similar
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Similar
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Similar\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5611,[Response]: B.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 499:  50%|▌| 500/1000 
[Running Accuracy]: 0.5620,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 500:  50%|▌| 500/1000 [05:31<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Similar\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The trees in the first image
B. The right sky in the second image
C. The building in the second image
D. The ground in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The trees in the first image
B. The right sky in the second image
C. The building in the second image
D. The ground in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The trees in the first image\nB. The right sky in the second image\nC. The building in the second image\nD. The ground in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5620,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 500:  50%|▌| 501/1000 [05:32<
[Running Accuracy]: 0.5629,[Response]: B.<|endoftext|>, [Correct Ans]: The right sky in the second image, , [Prog]: 501
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The trees in the first image\nB. The right sky in the second image\nC. The building in the second image\nD. The ground in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Less clear
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Less clear
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Less clear\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5629,[Response]: B.<|endoftext|>, [Correct Ans]: The right sky in the second image, , [Prog]: 501
[Running Accuracy]: 0.5637,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 502:  50%|▌| 502/1000 [05:32<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Less clear\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>In comparison to the first image, how is the clarity of the second image?
A. Similar
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:In comparison to the first image, how is the clarity of the second image?
A. Similar
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.

prompts: [["In comparison to the first image, how is the clarity of the second image?\nA. Similar\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5637,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 502:  50%|▌| 503/1000 [05:33<
[Running Accuracy]: 0.5646,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 503:  50%|▌| 503/1000 [05:33
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>In comparison to the first image, how is the clarity of the second image?\nA. Similar\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. More blurry
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. More blurry
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5646,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 503:  50%|▌| 504/1000 [05:33
[Running Accuracy]: 0.5655,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 504:  50%|▌| 504/1000 [05:33<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5655,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 504:  50%|▌| 505/1000 [05:34<
[Running Accuracy]: 0.5663,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 505:  50%|▌| 505/1000 [05:34<04:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the color richness of the second image?
A. Less rich
B. About the same
C. Richer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the color richness of the second image?
A. Less rich
B. About the same
C. Richer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the color richness of the second image?\nA. Less rich\nB. About the same\nC. Richer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5663,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 505:  51%|▌| 506/1000 [05:35<05:4
[Running Accuracy]: 0.5672,[Response]: A.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 506:  51%|▌| 506/1000 [05:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the color richness of the second image?\nA. Less rich\nB. About the same\nC. Richer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Clearer
B. Blurrier
C. Similar
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Clearer
B. Blurrier
C. Similar
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Clearer\nB. Blurrier\nC. Similar\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5672,[Response]: A.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 506:  51%|▌| 507/1000 [05:3
[Running Accuracy]: 0.5680,[Response]: A.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 507:  51%|▌| 507/1000 [05:35<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Clearer\nB. Blurrier\nC. Similar\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most severely affected by noise?
A. Background of the first image
B. Character in the second image
C. Character in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most severely affected by noise?
A. Background of the first image
B. Character in the second image
C. Character in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most severely affected by noise?\nA. Background of the first image\nB. Character in the second image\nC. Character in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5680,[Response]: A.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 507:  51%|▌| 508/1000 [05:36<
[Running Accuracy]: 0.5669,[Response]: A.<|endoftext|>, [Correct Ans]: Character in the second image, , [Prog]: 508:  5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most severely affected by noise?\nA. Background of the first image\nB. Character in the second image\nC. Character in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the texture detail of the second image?
A. Less rich
B. Similar
C. More rich
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the texture detail of the second image?
A. Less rich
B. Similar
C. More rich
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the texture detail of the second image?\nA. Less rich\nB. Similar\nC. More rich\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5669,[Response]: A.<|endoftext|>, [Correct Ans]: Character in the second image, , [Prog]: 508:  5
[Running Accuracy]: 0.5678,[Response]: C.<|endoftext|>, [Correct Ans]: More rich, , [Prog]: 509:  51%|▌| 509/1000 [05:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the texture detail of the second image?\nA. Less rich\nB. Similar\nC. More rich\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how does the authenticity of the second image compare?
A. Less authentic
B. About the same
C. More authentic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how does the authenticity of the second image compare?
A. Less authentic
B. About the same
C. More authentic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how does the authenticity of the second image compare?\nA. Less authentic\nB. About the same\nC. More authentic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5678,[Response]: C.<|endoftext|>, [Correct Ans]: More rich, , [Prog]: 509:  51%|▌| 510/1000 [05:3
[Running Accuracy]: 0.5667,[Response]: C.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 510:  51%|▌| 510/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how does the authenticity of the second image compare?\nA. Less authentic\nB. About the same\nC. More authentic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. More blurry
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. More blurry
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5667,[Response]: C.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 510:  51%|▌| 511/1000 
[Running Accuracy]: 0.5656,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 511:  51%|▌| 511/1000 [05
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is more severely affected by noise?
A. The bird in the second image
B. The background in the first image
C. The background in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is more severely affected by noise?
A. The bird in the second image
B. The background in the first image
C. The background in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is more severely affected by noise?\nA. The bird in the second image\nB. The background in the first image\nC. The background in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5656,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 511:  51%|▌| 512/1000 [05
[Running Accuracy]: 0.5664,[Response]: B.<|endoftext|>, [Correct Ans]: The background in the first image, , [Prog]: 512
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is more severely affected by noise?\nA. The bird in the second image\nB. The background in the first image\nC. The background in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, what is the color vividness of the second image?
A. More monotonous
B. About the same
C. More rich
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, what is the color vividness of the second image?
A. More monotonous
B. About the same
C. More rich
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, what is the color vividness of the second image?\nA. More monotonous\nB. About the same\nC. More rich\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5664,[Response]: B.<|endoftext|>, [Correct Ans]: The background in the first image, , [Prog]: 512
[Running Accuracy]: 0.5653,[Response]: C.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 513:  51%|▌| 513/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, what is the color vividness of the second image?\nA. More monotonous\nB. About the same\nC. More rich\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images somewhat blurry?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images somewhat blurry?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images somewhat blurry?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5653,[Response]: C.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 513:  51%|▌| 514/1000
[Running Accuracy]: 0.5661,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 514:  51%|▌| 514/1000 [05:39<04:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images somewhat blurry?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the sharpness of the second image?
A. Sharper
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the sharpness of the second image?
A. Sharper
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the sharpness of the second image?\nA. Sharper\nB. Blurrier\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5661,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 514:  52%|▌| 515/1000 [05:40<05:0
[Running Accuracy]: 0.5670,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 515:  52%|▌| 515/1000 [05:40
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the sharpness of the second image?\nA. Sharper\nB. Blurrier\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Similar
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Similar
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Similar\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5670,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 515:  52%|▌| 516/1000 [05:41
[Running Accuracy]: 0.5678,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 516:  52%|▌| 516/1000 [05:41
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Similar\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the lighting of the second image?
A. Similar
B. Less sufficient
C. More sufficient
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the lighting of the second image?
A. Similar
B. Less sufficient
C. More sufficient
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the lighting of the second image?\nA. Similar\nB. Less sufficient\nC. More sufficient\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5678,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 516:  52%|▌| 517/1000 [05:41
[Running Accuracy]: 0.5687,[Response]: C.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 517:  52%|▌| 517/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the lighting of the second image?\nA. Similar\nB. Less sufficient\nC. More sufficient\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the texture detail in the second image?
A. More abundant
B. Less abundant
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the texture detail in the second image?
A. More abundant
B. Less abundant
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the texture detail in the second image?\nA. More abundant\nB. Less abundant\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5687,[Response]: C.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 517:  52%|▌| 518/1000
[Running Accuracy]: 0.5676,[Response]: B.<|endoftext|>, [Correct Ans]: More abundant, , [Prog]: 518:  52%|▌| 518/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the texture detail in the second image?\nA. More abundant\nB. Less abundant\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the illumination sufficient in these two images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the illumination sufficient in these two images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the illumination sufficient in these two images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5676,[Response]: B.<|endoftext|>, [Correct Ans]: More abundant, , [Prog]: 518:  52%|▌| 519/1000 [
[Running Accuracy]: 0.5684,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 519:  52%|▌| 519/1000 [05:42<04:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the illumination sufficient in these two images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very realistic?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very realistic?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very realistic?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5684,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 519:  52%|▌| 520/1000 [05:43<04:3
[Running Accuracy]: 0.5692,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 520:  52%|▌| 520/1000 [05:43<04:34
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very realistic?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the texture detail of the first image richer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the texture detail of the first image richer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the texture detail of the first image richer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5692,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 520:  52%|▌| 521/1000 [05:44<04:31
[Running Accuracy]: 0.5701,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 521:  52%|▌| 521/1000 [05:44<04:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the texture detail of the first image richer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5701,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 521:  52%|▌| 522/1000 [05:44<04:2
[Running Accuracy]: 0.5709,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 522:  52%|▌| 522/1000 [05:44<04:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image more abundant than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image more abundant than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image more abundant than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5709,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 522:  52%|▌| 523/1000 [05:45<04:3
[Running Accuracy]: 0.5717,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 523:  52%|▌| 523/1000 [05:45<04:37
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image more abundant than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5717,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 523:  52%|▌| 524/1000 [05:45<04:38
[Running Accuracy]: 0.5706,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 524:  52%|▌| 524/1000 [05:45<04:38
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5706,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 524:  52%|▌| 525/1000 [05:46<04:53
[Running Accuracy]: 0.5695,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 525:  52%|▌| 525/1000 [05:46<04:53
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5695,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 525:  53%|▌| 526/1000 [05:47<04:40
[Running Accuracy]: 0.5684,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 526:  53%|▌| 526/1000 [05:47<04:40
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5684,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 526:  53%|▌| 527/1000 [05:47<04:30
[Running Accuracy]: 0.5674,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 527:  53%|▌| 527/1000 [05:47<04:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the texture detail of the first image richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the texture detail of the first image richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the texture detail of the first image richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5674,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 527:  53%|▌| 528/1000 [05:48<04:2
[Running Accuracy]: 0.5682,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 528:  53%|▌| 528/1000 [05:48<04:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the texture detail of the first image richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5682,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 528:  53%|▌| 529/1000 [05:48<04:1
[Running Accuracy]: 0.5690,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 529:  53%|▌| 529/1000 [05:48<04:18
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5690,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 529:  53%|▌| 530/1000 [05:49<04:15
[Running Accuracy]: 0.5698,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 530:  53%|▌| 530/1000 [05:49<04:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which of the following images has a serious overexposure issue?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which of the following images has a serious overexposure issue?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following images has a serious overexposure issue?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5698,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 530:  53%|▌| 531/1000 [05:49<04:1
[Running Accuracy]: 0.5687,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 531:  53%|▌| 531/1000 [05
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which of the following images has a serious overexposure issue?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5687,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 531:  53%|▌| 532/1000 [05
[Running Accuracy]: 0.5695,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 532:  53%|▌| 532/1000 [05:50<04:46
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image blurrier than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image blurrier than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image blurrier than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5695,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 532:  53%|▌| 533/1000 [05:50<04:38
[Running Accuracy]: 0.5704,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 533:  53%|▌| 533/1000 [05:50<04:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image blurrier than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very blurry?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very blurry?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very blurry?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5704,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 533:  53%|▌| 534/1000 [05:51<04:2
[Running Accuracy]: 0.5693,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 534:  53%|▌| 534/1000 [05:51<04:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very blurry?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5693,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 534:  54%|▌| 535/1000 [05:52<04:5
[Running Accuracy]: 0.5682,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 535:  54%|▌| 535/1000 [05:52<04:59
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5682,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 535:  54%|▌| 536/1000 [05:52<04:45
[Running Accuracy]: 0.5672,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 536:  54%|▌| 536/1000 [05:52<04:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image richer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image richer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image richer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5672,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 536:  54%|▌| 537/1000 [05:53<04:3
[Running Accuracy]: 0.5680,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 537:  54%|▌| 537/1000 [05:53<04:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image richer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5680,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 537:  54%|▌| 538/1000 [05:53<04:2
[Running Accuracy]: 0.5669,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 538:  54%|▌| 538/1000 [05:53<04:23
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images relatively sharp?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images relatively sharp?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images relatively sharp?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5000], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5669,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 538:  54%|▌| 539/1000 [05:54<04:19
[Running Accuracy]: 0.5677,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 539:  54%|▌| 539/1000 [05:54<04:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images relatively sharp?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5677,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 539:  54%|▌| 540/1000 [05:55<04:1
[Running Accuracy]: 0.5667,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 540:  54%|▌| 540/1000 [05:55<04:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image richer than that of the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image richer than that of the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image richer than that of the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5667,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 540:  54%|▌| 541/1000 [05:55<04:1
[Running Accuracy]: 0.5675,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 541:  54%|▌| 541/1000 [05:55<04:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image richer than that of the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5675,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 541:  54%|▌| 542/1000 [05:56<04:1
[Running Accuracy]: 0.5664,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 542:  54%|▌| 542/1000 [05:56<04:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the lighting of the first image more sufficient than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the lighting of the first image more sufficient than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the lighting of the first image more sufficient than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5664,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 542:  54%|▌| 543/1000 [05:56<04:1
[Running Accuracy]: 0.5672,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 543:  54%|▌| 543/1000 [05:56<04:14
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the lighting of the first image more sufficient than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most severely affected by noise?
A. The person in the first image
B. The ground in the first image
C. The hand in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most severely affected by noise?
A. The person in the first image
B. The ground in the first image
C. The hand in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most severely affected by noise?\nA. The person in the first image\nB. The ground in the first image\nC. The hand in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5672,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 543:  54%|▌| 544/1000 [05:57<04:17
[Running Accuracy]: 0.5662,[Response]: A.<|endoftext|>, [Correct Ans]: The hand in the second image, , [Prog]: 544:  54
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most severely affected by noise?\nA. The person in the first image\nB. The ground in the first image\nC. The hand in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5662,[Response]: A.<|endoftext|>, [Correct Ans]: The hand in the second image, , [Prog]: 544:  55
[Running Accuracy]: 0.5651,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 545:  55%|▌| 545/1000 [05:58<04:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the texture details of these two images both rich?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the texture details of these two images both rich?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the texture details of these two images both rich?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5651,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 545:  55%|▌| 546/1000 [05:58<04:4
[Running Accuracy]: 0.5659,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 546:  55%|▌| 546/1000 [05:58<04:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the texture details of these two images both rich?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the illumination of the first image more sufficient than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the illumination of the first image more sufficient than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the illumination of the first image more sufficient than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5659,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 546:  55%|▌| 547/1000 [05:59<04:2
[Running Accuracy]: 0.5649,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 547:  55%|▌| 547/1000 [05:59<04:29
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the illumination of the first image more sufficient than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5649,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 547:  55%|▌| 548/1000 [05:59<04:39
[Running Accuracy]: 0.5657,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 548:  55%|▌| 548/1000 [05:59<04:39
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5657,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 548:  55%|▌| 549/1000 [06:00<04:35
[Running Accuracy]: 0.5647,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 549:  55%|▌| 549/1000 [06:00<04:35
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5647,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 549:  55%|▌| 550/1000 [06:01<04:39
[Running Accuracy]: 0.5636,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 550:  55%|▌| 550/1000 [06:01<04:39
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5636,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 550:  55%|▌| 551/1000 [06:01<04:31
[Running Accuracy]: 0.5644,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 551:  55%|▌| 551/1000 [06:01<04:31
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5644,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 551:  55%|▌| 552/1000 [06:02<04:28
[Running Accuracy]: 0.5634,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 552:  55%|▌| 552/1000 [06:02<04:28
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image below looks more realistic?
A. the first image
B. the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image below looks more realistic?
A. the first image
B. the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which image below looks more realistic?\nA. the first image\nB. the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5634,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 552:  55%|▌| 553/1000 [06:02<04:23
[Running Accuracy]: 0.5624,[Response]: A.<|endoftext|>, [Correct Ans]: the second image, , [Prog]: 553:  55%|▌| 553/100
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image below looks more realistic?\nA. the first image\nB. the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5624,[Response]: A.<|endoftext|>, [Correct Ans]: the second image, , [Prog]: 553:  55%|▌| 554/100
[Running Accuracy]: 0.5632,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 554:  55%|▌| 554/1000 [06:03<04:19
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image has higher authenticity?
A. the first image
B. the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image has higher authenticity?
A. the first image
B. the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which image has higher authenticity?\nA. the first image\nB. the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5632,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 554:  56%|▌| 555/1000 [06:03<04:09
[Running Accuracy]: 0.5622,[Response]: A.<|endoftext|>, [Correct Ans]: the second image, , [Prog]: 555:  56%|▌| 555/100
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image has higher authenticity?\nA. the first image\nB. the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5622,[Response]: A.<|endoftext|>, [Correct Ans]: the second image, , [Prog]: 555:  56%|▌| 556/100
[Running Accuracy]: 0.5612,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 556:  56%|▌| 556/1000 [06:04<04:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the texture detail of the first image richer than that of the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the texture detail of the first image richer than that of the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the texture detail of the first image richer than that of the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5612,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 556:  56%|▌| 557/1000 [06:05<04:0
[Running Accuracy]: 0.5619,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 557:  56%|▌| 557/1000 [06:05<04:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the texture detail of the first image richer than that of the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image has more adequate lighting?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image has more adequate lighting?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.

prompts: [["Which image has more adequate lighting?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5619,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 557:  56%|▌| 558/1000 [06:05<04:0
[Running Accuracy]: 0.5627,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 558:  56%|▌| 558/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image has more adequate lighting?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5627,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 558:  56%|▌| 559/1000 [0
[Running Accuracy]: 0.5635,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 559:  56%|▌| 559/1000 [06:06<04:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the texture detail of the first image richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the texture detail of the first image richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the texture detail of the first image richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.4844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5635,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 559:  56%|▌| 560/1000 [06:06<04:1
[Running Accuracy]: 0.5625,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 560:  56%|▌| 560/1000 [06:06<04:16
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the texture detail of the first image richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5625,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 560:  56%|▌| 561/1000 [06:07<04:08
[Running Accuracy]: 0.5633,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 561:  56%|▌| 561/1000 [06:07<04:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The sun in the first image
B. The horse in the second image
C. The sky in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The sun in the first image
B. The horse in the second image
C. The sky in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The sun in the first image\nB. The horse in the second image\nC. The sky in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5633,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 561:  56%|▌| 562/1000 [06:07<04:0
[Running Accuracy]: 0.5641,[Response]: C.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 562:  56%
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The sun in the first image\nB. The horse in the second image\nC. The sky in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. the ground in the second image
B. the person in the first image
C. the sky in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. the ground in the second image
B. the person in the first image
C. the sky in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. the ground in the second image\nB. the person in the first image\nC. the sky in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5641,[Response]: C.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 562:  56%
[Running Accuracy]: 0.5648,[Response]: C.<|endoftext|>, [Correct Ans]: the sky in the second image, , [Prog]: 563:  56%
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. the ground in the second image\nB. the person in the first image\nC. the sky in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5648,[Response]: C.<|endoftext|>, [Correct Ans]: the sky in the second image, , [Prog]: 563:  56%
[Running Accuracy]: 0.5638,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 564:  56%|▌| 564/1000 [06:08<03:59
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5000], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5638,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 564:  56%|▌| 565/1000 [06:09<03:56
[Running Accuracy]: 0.5646,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 565:  56%|▌| 565/1000 [06:09<03:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5646,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 565:  57%|▌| 566/1000 [06:10<03:5
[Running Accuracy]: 0.5636,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 566:  57%|▌| 566/1000 [06:10<03:56
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images showing obvious overexposure?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images showing obvious overexposure?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images showing obvious overexposure?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5636,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 566:  57%|▌| 567/1000 [06:11<05:48
[Running Accuracy]: 0.5644,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 567:  57%|▌| 567/1000 [06:11<05:48
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images showing obvious overexposure?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below presents a serious out-of-focus problem?
A. ship in the second image
B. red foliage in the background of the first image
C. sky in the second image
D. pale yellow leaves in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below presents a serious out-of-focus problem?
A. ship in the second image
B. red foliage in the background of the first image
C. sky in the second image
D. pale yellow leaves in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below presents a serious out-of-focus problem?\nA. ship in the second image\nB. red foliage in the background of the first image\nC. sky in the second image\nD. pale yellow leaves in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.7188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5644,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 567:  57%|▌| 568/1000 [06:12<05:45
[Running Accuracy]: 0.5634,[Response]: A.<|endoftext|>, [Correct Ans]: red foliage in the background of the first image
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below presents a serious out-of-focus problem?\nA. ship in the second image\nB. red foliage in the background of the first image\nC. sky in the second image\nD. pale yellow leaves in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how rich is the color in the second image?
A. much worse
B. much richer
C. about the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how rich is the color in the second image?
A. much worse
B. much richer
C. about the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how rich is the color in the second image?\nA. much worse\nB. much richer\nC. about the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5634,[Response]: A.<|endoftext|>, [Correct Ans]: red foliage in the background of the first image
[Running Accuracy]: 0.5624,[Response]: A.<|endoftext|>, [Correct Ans]: about the same, , [Prog]: 569:  57%|▌| 569/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how rich is the color in the second image?\nA. much worse\nB. much richer\nC. about the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below shows the most obvious overexposure?
A. The trunk of the second image
B. The light bulb of the first image
C. The plane in the second image
D. The man wearing gray in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below shows the most obvious overexposure?
A. The trunk of the second image
B. The light bulb of the first image
C. The plane in the second image
D. The man wearing gray in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below shows the most obvious overexposure?\nA. The trunk of the second image\nB. The light bulb of the first image\nC. The plane in the second image\nD. The man wearing gray in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5624,[Response]: A.<|endoftext|>, [Correct Ans]: about the same, , [Prog]: 569:  57%|▌| 570/1000 
[Running Accuracy]: 0.5632,[Response]: B.<|endoftext|>, [Correct Ans]: The light bulb of the first image, , [Prog]: 570
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below shows the most obvious overexposure?\nA. The trunk of the second image\nB. The light bulb of the first image\nC. The plane in the second image\nD. The man wearing gray in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the lighting in the second image?
A. The lighting is much worse
B. About the same
C. The lighting is much stronger
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the lighting in the second image?
A. The lighting is much worse
B. About the same
C. The lighting is much stronger
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the lighting in the second image?\nA. The lighting is much worse\nB. About the same\nC. The lighting is much stronger\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5632,[Response]: B.<|endoftext|>, [Correct Ans]: The light bulb of the first image, , [Prog]: 570
[Running Accuracy]: 0.5639,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 571:  57%|▌| 571/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the lighting in the second image?\nA. The lighting is much worse\nB. About the same\nC. The lighting is much stronger\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how do you rate the authenticity of the first image?
A. Much higher authenticity
B. About the same
C. Much lower authenticity
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how do you rate the authenticity of the first image?
A. Much higher authenticity
B. About the same
C. Much lower authenticity
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how do you rate the authenticity of the first image?\nA. Much higher authenticity\nB. About the same\nC. Much lower authenticity\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5639,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 571:  57%|▌| 572/1000 
[Running Accuracy]: 0.5629,[Response]: C.<|endoftext|>, [Correct Ans]: Much higher authenticity, , [Prog]: 572:  57%|▌|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how do you rate the authenticity of the first image?\nA. Much higher authenticity\nB. About the same\nC. Much lower authenticity\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5629,[Response]: C.<|endoftext|>, [Correct Ans]: Much higher authenticity, , [Prog]: 572:  57%|▌|
[Running Accuracy]: 0.5620,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 573:  57%|▌| 573/1000 [06:16<05:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more blurry than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more blurry than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more blurry than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5620,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 573:  57%|▌| 574/1000 [06:17<05:4
[Running Accuracy]: 0.5610,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 574:  57%|▌| 574/1000 [06:17<05:45
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more blurry than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the illumination in the second image?
A. About the same
B. Stronger
C. Much worse
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the illumination in the second image?
A. About the same
B. Stronger
C. Much worse
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the illumination in the second image?\nA. About the same\nB. Stronger\nC. Much worse\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5610,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 574:  57%|▌| 575/1000 [06:18<05:53
[Running Accuracy]: 0.5617,[Response]: C.<|endoftext|>, [Correct Ans]: Much worse, , [Prog]: 575:  57%|▌| 575/1000 [06:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the illumination in the second image?\nA. About the same\nB. Stronger\nC. Much worse\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below has not been affected by overexposure?
A. The sky in the first image
B. The outdoor unit of the air conditioner in the first image
C. The bottom right corner of the desktop in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below has not been affected by overexposure?
A. The sky in the first image
B. The outdoor unit of the air conditioner in the first image
C. The bottom right corner of the desktop in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below has not been affected by overexposure?\nA. The sky in the first image\nB. The outdoor unit of the air conditioner in the first image\nC. The bottom right corner of the desktop in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5617,[Response]: C.<|endoftext|>, [Correct Ans]: Much worse, , [Prog]: 575:  58%|▌| 576/1000 [06:
[Running Accuracy]: 0.5608,[Response]: C.<|endoftext|>, [Correct Ans]: The outdoor unit of the air conditioner in the f
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below has not been affected by overexposure?\nA. The sky in the first image\nB. The outdoor unit of the air conditioner in the first image\nC. The bottom right corner of the desktop in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the composition of the first image better than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the composition of the first image better than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the composition of the first image better than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5608,[Response]: C.<|endoftext|>, [Correct Ans]: The outdoor unit of the air conditioner in the f
[Running Accuracy]: 0.5598,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 577:  58%|▌| 577/1000 [06:19<06:04
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the composition of the first image better than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the second image richer than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the second image richer than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the second image richer than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5598,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 577:  58%|▌| 578/1000 [06:20<06:38
[Running Accuracy]: 0.5606,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 578:  58%|▌| 578/1000 [06:20<06:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the second image richer than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both images underexposed?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both images underexposed?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both images underexposed?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5606,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 578:  58%|▌| 579/1000 [06:21<06:2
[Running Accuracy]: 0.5596,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 579:  58%|▌| 579/1000 [06:21<06:29
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both images underexposed?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the focus of the first image better than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the focus of the first image better than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the focus of the first image better than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5596,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 579:  58%|▌| 580/1000 [06:22<06:17
[Running Accuracy]: 0.5586,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 580:  58%|▌| 580/1000 [06:22<06:17
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the focus of the first image better than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5586,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 580:  58%|▌| 581/1000 [06:23<06:04
[Running Accuracy]: 0.5577,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 581:  58%|▌| 581/1000 [06:23<06:04
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The background of the first image
B. The small dog doll in the second image
C. The background of the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The background of the first image
B. The small dog doll in the second image
C. The background of the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The background of the first image\nB. The small dog doll in the second image\nC. The background of the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5577,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 581:  58%|▌| 582/1000 [06:24<06:14
[Running Accuracy]: 0.5584,[Response]: A.<|endoftext|>, [Correct Ans]: The background of the first image, , [Prog]: 582
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The background of the first image\nB. The small dog doll in the second image\nC. The background of the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image richer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image richer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image richer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5584,[Response]: A.<|endoftext|>, [Correct Ans]: The background of the first image, , [Prog]: 582
[Running Accuracy]: 0.5575,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 583:  58%|▌| 583/1000 [06:25<05:53
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image richer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is affected by motion blur?
A. the child in the second image
B. the red vehicle in the first image
C. the house in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is affected by motion blur?
A. the child in the second image
B. the red vehicle in the first image
C. the house in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is affected by motion blur?\nA. the child in the second image\nB. the red vehicle in the first image\nC. the house in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5575,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 583:  58%|▌| 584/1000 [06:25<05:47
[Running Accuracy]: 0.5582,[Response]: A.<|endoftext|>, [Correct Ans]: the child in the second image, , [Prog]: 584:  5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is affected by motion blur?\nA. the child in the second image\nB. the red vehicle in the first image\nC. the house in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the lighting of the first image significantly better than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the lighting of the first image significantly better than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the lighting of the first image significantly better than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5582,[Response]: A.<|endoftext|>, [Correct Ans]: the child in the second image, , [Prog]: 584:  5
[Running Accuracy]: 0.5590,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 585:  58%|▌| 585/1000 [06:26<05:41
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the lighting of the first image significantly better than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how rich are the colors in the second image?
A. Much better
B. Much worse
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how rich are the colors in the second image?
A. Much better
B. Much worse
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how rich are the colors in the second image?\nA. Much better\nB. Much worse\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5590,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 585:  59%|▌| 586/1000 [06:27<06:09
[Running Accuracy]: 0.5580,[Response]: C.<|endoftext|>, [Correct Ans]: Much worse, , [Prog]: 586:  59%|▌| 586/1000 [06:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how rich are the colors in the second image?\nA. Much better\nB. Much worse\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image significantly less clear than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image significantly less clear than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image significantly less clear than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5580,[Response]: C.<|endoftext|>, [Correct Ans]: Much worse, , [Prog]: 586:  59%|▌| 587/1000 [06:
[Running Accuracy]: 0.5588,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 587:  59%|▌| 587/1000 [06:28<06:16
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image significantly less clear than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the composition of the second image?
A. Much worse
B. About the same
C. Much better
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the composition of the second image?
A. Much worse
B. About the same
C. Much better
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the composition of the second image?\nA. Much worse\nB. About the same\nC. Much better\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5588,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 587:  59%|▌| 588/1000 [06:29<06:06
[Running Accuracy]: 0.5595,[Response]: C.<|endoftext|>, [Correct Ans]: Much better, , [Prog]: 588:  59%|▌| 588/1000 [06
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the composition of the second image?\nA. Much worse\nB. About the same\nC. Much better\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What kind of distortion is not present in the two images below?
A. overexposure
B. motion blur
C. noise
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What kind of distortion is not present in the two images below?
A. overexposure
B. motion blur
C. noise
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion is not present in the two images below?\nA. overexposure\nB. motion blur\nC. noise\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5595,[Response]: C.<|endoftext|>, [Correct Ans]: Much better, , [Prog]: 588:  59%|▌| 589/1000 [06
[Running Accuracy]: 0.5586,[Response]: A.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 589:  59%|▌| 589/1000 [06
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What kind of distortion is not present in the two images below?\nA. overexposure\nB. motion blur\nC. noise\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the second image clearer than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the second image clearer than the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the second image clearer than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5586,[Response]: A.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 589:  59%|▌| 590/1000 [06
[Running Accuracy]: 0.5593,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 590:  59%|▌| 590/1000 [06:31<06:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the second image clearer than the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how rich is the color of the second image?
A. Richer
B. More monotonous
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how rich is the color of the second image?
A. Richer
B. More monotonous
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how rich is the color of the second image?\nA. Richer\nB. More monotonous\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5593,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 590:  59%|▌| 591/1000 [06:32<05:5
[Running Accuracy]: 0.5601,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 591:  59%|▌| 591/1000 [06:32<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how rich is the color of the second image?\nA. Richer\nB. More monotonous\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by out-of-focus?
A. The pedestrian in the second image
B. The houses in the background of the second image
C. The white dog in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by out-of-focus?
A. The pedestrian in the second image
B. The houses in the background of the second image
C. The white dog in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by out-of-focus?\nA. The pedestrian in the second image\nB. The houses in the background of the second image\nC. The white dog in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5601,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 591:  59%|▌| 592/1000 [06:32<0
[Running Accuracy]: 0.5591,[Response]: A.<|endoftext|>, [Correct Ans]: The white dog in the first image, , [Prog]: 592:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by out-of-focus?\nA. The pedestrian in the second image\nB. The houses in the background of the second image\nC. The white dog in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most affected by motion blur?
A. Background of the first image
B. Athlete swinging a club in the second image
C. Trunk of the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most affected by motion blur?
A. Background of the first image
B. Athlete swinging a club in the second image
C. Trunk of the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most affected by motion blur?\nA. Background of the first image\nB. Athlete swinging a club in the second image\nC. Trunk of the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B

[Running Accuracy]: 0.5591,[Response]: A.<|endoftext|>, [Correct Ans]: The white dog in the first image, , [Prog]: 592:
[Running Accuracy]: 0.5599,[Response]: B<|endoftext|>, [Correct Ans]: Athlete swinging a club in the second image, , [P
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most affected by motion blur?\nA. Background of the first image\nB. Athlete swinging a club in the second image\nC. Trunk of the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How is the focus of the second image relative to the first image?
A. Similar
B. Much worse
C. Much better
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How is the focus of the second image relative to the first image?
A. Similar
B. Much worse
C. Much better
Answer with the option's letter from the given choices directly.

prompts: [["How is the focus of the second image relative to the first image?\nA. Similar\nB. Much worse\nC. Much better\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5599,[Response]: B<|endoftext|>, [Correct Ans]: Athlete swinging a club in the second image, , [P
[Running Accuracy]: 0.5589,[Response]: B.<|endoftext|>, [Correct Ans]: Much better, , [Prog]: 594:  59%|▌| 594/1000 [06
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How is the focus of the second image relative to the first image?\nA. Similar\nB. Much worse\nC. Much better\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the noise in the first image more severe than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the noise in the first image more severe than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the noise in the first image more severe than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5589,[Response]: B.<|endoftext|>, [Correct Ans]: Much better, , [Prog]: 594:  60%|▌| 595/1000 [06
[Running Accuracy]: 0.5597,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 595:  60%|▌| 595/1000 [06:35<05:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the noise in the first image more severe than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the richness of colors in the second image?
A. More monotonous
B. More rich
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the richness of colors in the second image?
A. More monotonous
B. More rich
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the richness of colors in the second image?\nA. More monotonous\nB. More rich\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5597,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 595:  60%|▌| 596/1000 [06:36<05:3
[Running Accuracy]: 0.5604,[Response]: B.<|endoftext|>, [Correct Ans]: More rich, , [Prog]: 596:  60%|▌| 596/1000 [06:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the richness of colors in the second image?\nA. More monotonous\nB. More rich\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5604,[Response]: B.<|endoftext|>, [Correct Ans]: More rich, , [Prog]: 596:  60%|▌| 597/1000 [06:3
[Running Accuracy]: 0.5595,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 597:  60%|▌| 597/1000 [06:37<05:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below has the most severe overexposure?
A. The background of the first image
B. The trees in the second image
C. The signboard in the first image
D. The house in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below has the most severe overexposure?
A. The background of the first image
B. The trees in the second image
C. The signboard in the first image
D. The house in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below has the most severe overexposure?\nA. The background of the first image\nB. The trees in the second image\nC. The signboard in the first image\nD. The house in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5595,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 597:  60%|▌| 598/1000 [06:37<05:0
[Running Accuracy]: 0.5602,[Response]: A.<|endoftext|>, [Correct Ans]: The background of the first image, , [Prog]: 598
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below has the most severe overexposure?\nA. The background of the first image\nB. The trees in the second image\nC. The signboard in the first image\nD. The house in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5602,[Response]: A.<|endoftext|>, [Correct Ans]: The background of the first image, , [Prog]: 598
[Running Accuracy]: 0.5593,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 599:  60%|▌| 599/1000 [06:38<05:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the illumination of the first image more sufficient than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the illumination of the first image more sufficient than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the illumination of the first image more sufficient than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5593,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 599:  60%|▌| 600/1000 [06:39<05:1
[Running Accuracy]: 0.5600,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 600:  60%|▌| 600/1000 [06:39<05:12
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the illumination of the first image more sufficient than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the focusing situation in the second image?
A. Similar
B. Better
C. Worse
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the focusing situation in the second image?
A. Similar
B. Better
C. Worse
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the focusing situation in the second image?\nA. Similar\nB. Better\nC. Worse\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5600,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 600:  60%|▌| 601/1000 [06:40<05:29
[Running Accuracy]: 0.5591,[Response]: C.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 601:  60%|▌| 601/1000 [06:40<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the focusing situation in the second image?\nA. Similar\nB. Better\nC. Worse\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-29.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5591,[Response]: C.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 601:  60%|▌| 602/1000 [06:40<0
[Running Accuracy]: 0.5581,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 602:  60%|▌| 602/1000 [06:40<05:05
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the realism of the second image compare to the first image?
A. Similar
B. Less realistic
C. More realistic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the realism of the second image compare to the first image?
A. Similar
B. Less realistic
C. More realistic
Answer with the option's letter from the given choices directly.

prompts: [["How does the realism of the second image compare to the first image?\nA. Similar\nB. Less realistic\nC. More realistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5581,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 602:  60%|▌| 603/1000 [06:41<04:37
[Running Accuracy]: 0.5589,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 603:  60%|▌| 603/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the realism of the second image compare to the first image?\nA. Similar\nB. Less realistic\nC. More realistic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the composition of the first image worse than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the composition of the first image worse than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the composition of the first image worse than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5589,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 603:  60%|▌| 604/1000 
[Running Accuracy]: 0.5596,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 604:  60%|▌| 604/1000 [06:42<04:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the composition of the first image worse than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images relatively blurry?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images relatively blurry?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images relatively blurry?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5596,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 604:  60%|▌| 605/1000 [06:43<05:3
[Running Accuracy]: 0.5603,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 605:  60%|▌| 605/1000 [06:43<05:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images relatively blurry?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What kind of distortion is not present in these two images?
A. Lens flare
B. Overexposure
C. Motion blur
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What kind of distortion is not present in these two images?
A. Lens flare
B. Overexposure
C. Motion blur
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion is not present in these two images?\nA. Lens flare\nB. Overexposure\nC. Motion blur\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5603,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 605:  61%|▌| 606/1000 [06:44<05:2
[Running Accuracy]: 0.5594,[Response]: B.<|endoftext|>, [Correct Ans]: Lens flare, , [Prog]: 606:  61%|▌| 606/1000 [06:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What kind of distortion is not present in these two images?\nA. Lens flare\nB. Overexposure\nC. Motion blur\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the illumination of the first image weaker than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the illumination of the first image weaker than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the illumination of the first image weaker than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5594,[Response]: B.<|endoftext|>, [Correct Ans]: Lens flare, , [Prog]: 606:  61%|▌| 607/1000 [06:
[Running Accuracy]: 0.5585,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 607:  61%|▌| 607/1000 [06:45<05:57
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the illumination of the first image weaker than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5585,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 607:  61%|▌| 608/1000 [06:45<05:36
[Running Accuracy]: 0.5592,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 608:  61%|▌| 608/1000 [06:45<05:36
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is not affected by the focus problem?
A. The wine glass in the second image
B. The background in the second image
C. The ground in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is not affected by the focus problem?
A. The wine glass in the second image
B. The background in the second image
C. The ground in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is not affected by the focus problem?\nA. The wine glass in the second image\nB. The background in the second image\nC. The ground in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5592,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 608:  61%|▌| 609/1000 [06:46<05:33
[Running Accuracy]: 0.5599,[Response]: A.<|endoftext|>, [Correct Ans]: The wine glass in the second image, , [Prog]: 60
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is not affected by the focus problem?\nA. The wine glass in the second image\nB. The background in the second image\nC. The ground in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how clear is the second image?
A. A bit blurry
B. About the same
C. Much clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how clear is the second image?
A. A bit blurry
B. About the same
C. Much clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how clear is the second image?\nA. A bit blurry\nB. About the same\nC. Much clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5599,[Response]: A.<|endoftext|>, [Correct Ans]: The wine glass in the second image, , [Prog]: 60
[Running Accuracy]: 0.5590,[Response]: C.<|endoftext|>, [Correct Ans]: A bit blurry, , [Prog]: 610:  61%|▌| 610/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how clear is the second image?\nA. A bit blurry\nB. About the same\nC. Much clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how rich is the texture detail in the second image?
A. Similar
B. Richer
C. Less rich
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how rich is the texture detail in the second image?
A. Similar
B. Richer
C. Less rich
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how rich is the texture detail in the second image?\nA. Similar\nB. Richer\nC. Less rich\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5590,[Response]: C.<|endoftext|>, [Correct Ans]: A bit blurry, , [Prog]: 610:  61%|▌| 611/1000 [0
[Running Accuracy]: 0.5597,[Response]: B.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 611:  61%|▌| 611/1000 [06:48<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how rich is the texture detail in the second image?\nA. Similar\nB. Richer\nC. Less rich\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the lighting in the second image?
A. Less Adequate
B. Similar
C. More Adequate
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the lighting in the second image?
A. Less Adequate
B. Similar
C. More Adequate
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the lighting in the second image?\nA. Less Adequate\nB. Similar\nC. More Adequate\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-29.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5597,[Response]: B.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 611:  61%|▌| 612/1000 [06:48<0
[Running Accuracy]: 0.5605,[Response]: C.<|endoftext|>, [Correct Ans]: More Adequate, , [Prog]: 612:  61%|▌| 612/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the lighting in the second image?\nA. Less Adequate\nB. Similar\nC. More Adequate\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5605,[Response]: C.<|endoftext|>, [Correct Ans]: More Adequate, , [Prog]: 612:  61%|▌| 613/1000 [
[Running Accuracy]: 0.5595,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 613:  61%|▌| 613/1000 [06:49<04:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part has the richest detail texture?
A. The floor in the first image
B. The ground in the second image
C. The hand holding a gun in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part has the richest detail texture?
A. The floor in the first image
B. The ground in the second image
C. The hand holding a gun in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the richest detail texture?\nA. The floor in the first image\nB. The ground in the second image\nC. The hand holding a gun in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5595,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 613:  61%|▌| 614/1000 [06:50<04:3
[Running Accuracy]: 0.5603,[Response]: C.<|endoftext|>, [Correct Ans]: The hand holding a gun in the second image, , [P
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part has the richest detail texture?\nA. The floor in the first image\nB. The ground in the second image\nC. The hand holding a gun in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image richer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image richer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image richer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5603,[Response]: C.<|endoftext|>, [Correct Ans]: The hand holding a gun in the second image, , [P
[Running Accuracy]: 0.5610,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 615:  62%|▌| 615/1000 [06:50<04:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image richer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is not affected by the focus issue?
A. The sky in the first image
B. The bottom lotus leaf in the second image
C. The building in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is not affected by the focus issue?
A. The sky in the first image
B. The bottom lotus leaf in the second image
C. The building in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is not affected by the focus issue?\nA. The sky in the first image\nB. The bottom lotus leaf in the second image\nC. The building in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5610,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 615:  62%|▌| 616/1000 [06:51<04:0
[Running Accuracy]: 0.5617,[Response]: B.<|endoftext|>, [Correct Ans]: The bottom lotus leaf in the second image, , [Pr
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is not affected by the focus issue?\nA. The sky in the first image\nB. The bottom lotus leaf in the second image\nC. The building in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by noise?
A. The ground in the first image
B. The sky in the second image
C. The sky in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by noise?
A. The ground in the first image
B. The sky in the second image
C. The sky in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by noise?\nA. The ground in the first image\nB. The sky in the second image\nC. The sky in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5617,[Response]: B.<|endoftext|>, [Correct Ans]: The bottom lotus leaf in the second image, , [Pr
[Running Accuracy]: 0.5608,[Response]: A.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 617:  62%
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by noise?\nA. The ground in the first image\nB. The sky in the second image\nC. The sky in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5608,[Response]: A.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 617:  62%
[Running Accuracy]: 0.5599,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 618:  62%|▌| 618/1000 [06:52<03:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the illumination of the second image?
A. Similar
B. More sufficient
C. Less sufficient
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the illumination of the second image?
A. Similar
B. More sufficient
C. Less sufficient
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the illumination of the second image?\nA. Similar\nB. More sufficient\nC. Less sufficient\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5599,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 618:  62%|▌| 619/1000 [06:53<04:4
[Running Accuracy]: 0.5590,[Response]: C.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 619:  62%|▌| 619/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the illumination of the second image?\nA. Similar\nB. More sufficient\nC. Less sufficient\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how would you rate the realism of the second image?
A. Less realistic
B. More realistic
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how would you rate the realism of the second image?
A. Less realistic
B. More realistic
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how would you rate the realism of the second image?\nA. Less realistic\nB. More realistic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5590,[Response]: C.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 619:  62%|▌| 620/1000
[Running Accuracy]: 0.5597,[Response]: A.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 620:  62%|▌| 620/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how would you rate the realism of the second image?\nA. Less realistic\nB. More realistic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image blurrier than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image blurrier than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image blurrier than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5597,[Response]: A.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 620:  62%|▌| 621/1000 
[Running Accuracy]: 0.5604,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 621:  62%|▌| 621/1000 [06:54<04:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image blurrier than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Did both of these images have overexposure issues?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Did both of these images have overexposure issues?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Did both of these images have overexposure issues?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5604,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 621:  62%|▌| 622/1000 [06:55<03:5
[Running Accuracy]: 0.5611,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 622:  62%|▌| 622/1000 [06:55<03:58
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Did both of these images have overexposure issues?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by overexposure?
A. the yellow doll in the first image
B. the street lamp in the second image
C. the wall in the first image
D. the vehicle in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by overexposure?
A. the yellow doll in the first image
B. the street lamp in the second image
C. the wall in the first image
D. the vehicle in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by overexposure?\nA. the yellow doll in the first image\nB. the street lamp in the second image\nC. the wall in the first image\nD. the vehicle in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5611,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 622:  62%|▌| 623/1000 [06:55<03:49
[Running Accuracy]: 0.5602,[Response]: B.<|endoftext|>, [Correct Ans]: the yellow doll in the first image, , [Prog]: 62
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by overexposure?\nA. the yellow doll in the first image\nB. the street lamp in the second image\nC. the wall in the first image\nD. the vehicle in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the lighting in the second image?
A. Weaker
B. About the same
C. More sufficient
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the lighting in the second image?
A. Weaker
B. About the same
C. More sufficient
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the lighting in the second image?\nA. Weaker\nB. About the same\nC. More sufficient\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5602,[Response]: B.<|endoftext|>, [Correct Ans]: the yellow doll in the first image, , [Prog]: 62
[Running Accuracy]: 0.5593,[Response]: B.<|endoftext|>, [Correct Ans]: Weaker, , [Prog]: 624:  62%|▌| 624/1000 [06:56<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the lighting in the second image?\nA. Weaker\nB. About the same\nC. More sufficient\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5593,[Response]: B.<|endoftext|>, [Correct Ans]: Weaker, , [Prog]: 624:  62%|▋| 625/1000 [06:57<0
[Running Accuracy]: 0.5584,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 625:  62%|▋| 625/1000 [06:57<03:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the illumination of the first image stronger than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the illumination of the first image stronger than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the illumination of the first image stronger than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5584,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 625:  63%|▋| 626/1000 [06:57<04:0
[Running Accuracy]: 0.5591,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 626:  63%|▋| 626/1000 [06:57<04:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the illumination of the first image stronger than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5591,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 626:  63%|▋| 627/1000 [06:58<03:5
[Running Accuracy]: 0.5582,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 627:  63%|▋| 627/1000 [06:58<03:55
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the composition of the second image compare to the first image?
A. Similar
B. Worse
C. Better
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the composition of the second image compare to the first image?
A. Similar
B. Worse
C. Better
Answer with the option's letter from the given choices directly.

prompts: [["How does the composition of the second image compare to the first image?\nA. Similar\nB. Worse\nC. Better\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5582,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 627:  63%|▋| 628/1000 [06:58<03:46
[Running Accuracy]: 0.5589,[Response]: C.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 628:  63%|▋| 628/1000 [06:58<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the composition of the second image compare to the first image?\nA. Similar\nB. Worse\nC. Better\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image more rich and vivid than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image more rich and vivid than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image more rich and vivid than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5589,[Response]: C.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 628:  63%|▋| 629/1000 [06:59<0
[Running Accuracy]: 0.5580,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 629:  63%|▋| 629/1000 [06:59<03:39
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image more rich and vivid than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. Sky in the second image
B. Person in the first image
C. Grassland in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. Sky in the second image
B. Person in the first image
C. Grassland in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. Sky in the second image\nB. Person in the first image\nC. Grassland in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5580,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 629:  63%|▋| 630/1000 [07:00<04:07
[Running Accuracy]: 0.5587,[Response]: A.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 630:  63%|▋| 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. Sky in the second image\nB. Person in the first image\nC. Grassland in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the color vividness of the second image?
A. Similar
B. More vivid
C. Less vivid
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the color vividness of the second image?
A. Similar
B. More vivid
C. Less vivid
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the color vividness of the second image?\nA. Similar\nB. More vivid\nC. Less vivid\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5587,[Response]: A.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 630:  63%|▋| 
[Running Accuracy]: 0.5594,[Response]: B.<|endoftext|>, [Correct Ans]: More vivid, , [Prog]: 631:  63%|▋| 631/1000 [07:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the color vividness of the second image?\nA. Similar\nB. More vivid\nC. Less vivid\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more authentic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more authentic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more authentic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5594,[Response]: B.<|endoftext|>, [Correct Ans]: More vivid, , [Prog]: 631:  63%|▋| 632/1000 [07:
[Running Accuracy]: 0.5601,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 632:  63%|▋| 632/1000 [07:01<03:58
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more authentic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5601,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 632:  63%|▋| 633/1000 [07:02<03:53
[Running Accuracy]: 0.5592,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 633:  63%|▋| 633/1000 [07:02<03:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how clear is the second image?
A. About the same
B. More blurry
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how clear is the second image?
A. About the same
B. More blurry
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how clear is the second image?\nA. About the same\nB. More blurry\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5592,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 633:  63%|▋| 634/1000 [07:02<03:4
[Running Accuracy]: 0.5584,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 634:  63%|▋| 634/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how clear is the second image?\nA. About the same\nB. More blurry\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the lighting in the second image?
A. Weaker
B. About the same
C. Stronger
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the lighting in the second image?
A. Weaker
B. About the same
C. Stronger
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the lighting in the second image?\nA. Weaker\nB. About the same\nC. Stronger\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5584,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 634:  64%|▋| 635/1000 
[Running Accuracy]: 0.5575,[Response]: B.<|endoftext|>, [Correct Ans]: Weaker, , [Prog]: 635:  64%|▋| 635/1000 [07:03<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the lighting in the second image?\nA. Weaker\nB. About the same\nC. Stronger\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5575,[Response]: B.<|endoftext|>, [Correct Ans]: Weaker, , [Prog]: 635:  64%|▋| 636/1000 [07:04<0
[Running Accuracy]: 0.5566,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 636:  64%|▋| 636/1000 [07:04<04:35
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how would you rate the clarity of the second image?
A. Blurrier
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how would you rate the clarity of the second image?
A. Blurrier
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how would you rate the clarity of the second image?\nA. Blurrier\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5566,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 636:  64%|▋| 637/1000 [07:04<04:09
[Running Accuracy]: 0.5573,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 637:  64%|▋| 637/1000 [07:04<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how would you rate the clarity of the second image?\nA. Blurrier\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5573,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 637:  64%|▋| 638/1000 [07:05<
[Running Accuracy]: 0.5580,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 638:  64%|▋| 638/1000 [07:05<04:18
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image less realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image less realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image less realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5580,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 638:  64%|▋| 639/1000 [07:06<03:58
[Running Accuracy]: 0.5587,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 639:  64%|▋| 639/1000 [07:06<03:58
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image less realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Did noise appear in both of these two images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Did noise appear in both of these two images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Did noise appear in both of these two images?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5587,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 639:  64%|▋| 640/1000 [07:06<03:50
[Running Accuracy]: 0.5578,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 640:  64%|▋| 640/1000 [07:06<03:50
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Did noise appear in both of these two images?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the colors of these two images both monotonous?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the colors of these two images both monotonous?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the colors of these two images both monotonous?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5578,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 640:  64%|▋| 641/1000 [07:07<03:38
[Running Accuracy]: 0.5585,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 641:  64%|▋| 641/1000 [07:07<03:38
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the colors of these two images both monotonous?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. More blurry
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. More blurry
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5585,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 641:  64%|▋| 642/1000 [07:07<03:35
[Running Accuracy]: 0.5592,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 642:  64%|▋| 642/1000 [07:07<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The ceiling light in the first image
B. The person in the first image
C. The motorcycle in the second image
D. The ground in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The ceiling light in the first image
B. The person in the first image
C. The motorcycle in the second image
D. The ground in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The ceiling light in the first image\nB. The person in the first image\nC. The motorcycle in the second image\nD. The ground in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5592,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 642:  64%|▋| 643/1000 [07:08<
[Running Accuracy]: 0.5599,[Response]: A.<|endoftext|>, [Correct Ans]: The ceiling light in the first image, , [Prog]: 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The ceiling light in the first image\nB. The person in the first image\nC. The motorcycle in the second image\nD. The ground in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how rich are the texture details in the second image?
A. Less rich
B. About the same
C. Richer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how rich are the texture details in the second image?
A. Less rich
B. About the same
C. Richer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how rich are the texture details in the second image?\nA. Less rich\nB. About the same\nC. Richer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-29.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5599,[Response]: A.<|endoftext|>, [Correct Ans]: The ceiling light in the first image, , [Prog]: 
[Running Accuracy]: 0.5590,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 644:  64%|▋| 644/1000 [07:09<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how rich are the texture details in the second image?\nA. Less rich\nB. About the same\nC. Richer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the lighting of the first image more sufficient than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the lighting of the first image more sufficient than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the lighting of the first image more sufficient than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5590,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 644:  64%|▋| 645/1000 [07:09<0
[Running Accuracy]: 0.5581,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 645:  64%|▋| 645/1000 [07:09<03:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the lighting of the first image more sufficient than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the lighting sufficient in both of the following images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the lighting sufficient in both of the following images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the lighting sufficient in both of the following images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5581,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 645:  65%|▋| 646/1000 [07:10<03:2
[Running Accuracy]: 0.5573,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 646:  65%|▋| 646/1000 [07:10<03:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the lighting sufficient in both of the following images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Clearer
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Clearer
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Clearer\nB. Blurrier\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5573,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 646:  65%|▋| 647/1000 [07:10<03:2
[Running Accuracy]: 0.5564,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 647:  65%|▋| 647/1000 [07:10
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Clearer\nB. Blurrier\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the sharpness of the second image?
A. About the same
B. Slightly sharper
C. Slightly more blurry
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the sharpness of the second image?
A. About the same
B. Slightly sharper
C. Slightly more blurry
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the sharpness of the second image?\nA. About the same\nB. Slightly sharper\nC. Slightly more blurry\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5000], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5564,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 647:  65%|▋| 648/1000 [07:11
[Running Accuracy]: 0.5556,[Response]: C.<|endoftext|>, [Correct Ans]: Slightly sharper, , [Prog]: 648:  65%|▋| 648/100
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the sharpness of the second image?\nA. About the same\nB. Slightly sharper\nC. Slightly more blurry\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how real is the second image?
A. More realistic
B. Less realistic
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how real is the second image?
A. More realistic
B. Less realistic
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how real is the second image?\nA. More realistic\nB. Less realistic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5556,[Response]: C.<|endoftext|>, [Correct Ans]: Slightly sharper, , [Prog]: 648:  65%|▋| 649/100
[Running Accuracy]: 0.5562,[Response]: B.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 649:  65%|▋| 649/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how real is the second image?\nA. More realistic\nB. Less realistic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the composition of the first image better than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the composition of the first image better than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the composition of the first image better than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5562,[Response]: B.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 649:  65%|▋| 650/1000 
[Running Accuracy]: 0.5569,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 650:  65%|▋| 650/1000 [07:12<03:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the composition of the first image better than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What kind of distortion do not appear in these two images?
A. Lens flare
B. Motion blur
C. Overexposure
D. Noise
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What kind of distortion do not appear in these two images?
A. Lens flare
B. Motion blur
C. Overexposure
D. Noise
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion do not appear in these two images?\nA. Lens flare\nB. Motion blur\nC. Overexposure\nD. Noise\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.5569,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 650:  65%|▋| 651/1000 [07:13<03:2
[Running Accuracy]: 0.5561,[Response]: D.<|endoftext|>, [Correct Ans]: Lens flare, , [Prog]: 651:  65%|▋| 651/1000 [07:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What kind of distortion do not appear in these two images?\nA. Lens flare\nB. Motion blur\nC. Overexposure\nD. Noise\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is least affected by noise?
A. The characters in the second image
B. The snow in the second image
C. The bridge in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is least affected by noise?
A. The characters in the second image
B. The snow in the second image
C. The bridge in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is least affected by noise?\nA. The characters in the second image\nB. The snow in the second image\nC. The bridge in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5561,[Response]: D.<|endoftext|>, [Correct Ans]: Lens flare, , [Prog]: 651:  65%|▋| 652/1000 [07:
[Running Accuracy]: 0.5567,[Response]: C.<|endoftext|>, [Correct Ans]: The bridge in the first image, , [Prog]: 652:  6
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is least affected by noise?\nA. The characters in the second image\nB. The snow in the second image\nC. The bridge in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how clear is the second image?
A. More blurry
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how clear is the second image?
A. More blurry
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how clear is the second image?\nA. More blurry\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5567,[Response]: C.<|endoftext|>, [Correct Ans]: The bridge in the first image, , [Prog]: 652:  6
[Running Accuracy]: 0.5574,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 653:  65%|▋| 653/1000 [07:14<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how clear is the second image?\nA. More blurry\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Relative to the first image, how clear is the second image?
A. Blurrier
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Relative to the first image, how clear is the second image?
A. Blurrier
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Relative to the first image, how clear is the second image?\nA. Blurrier\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5574,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 653:  65%|▋| 654/1000 [07:15<
[Running Accuracy]: 0.5581,[Response]: A.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 654:  65%|▋| 654/1000 [07:15
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Relative to the first image, how clear is the second image?\nA. Blurrier\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the realism of the second image compare to the first image?
A. Less realistic
B. About the same
C. More realistic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the realism of the second image compare to the first image?
A. Less realistic
B. About the same
C. More realistic
Answer with the option's letter from the given choices directly.

prompts: [["How does the realism of the second image compare to the first image?\nA. Less realistic\nB. About the same\nC. More realistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5581,[Response]: A.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 654:  66%|▋| 655/1000 [07:15
[Running Accuracy]: 0.5588,[Response]: A.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 655:  66%|▋| 655/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the realism of the second image compare to the first image?\nA. Less realistic\nB. About the same\nC. More realistic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are there any distortions in these two images?
A. Noise
B. Overexposure
C. Motion blur
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are there any distortions in these two images?
A. Noise
B. Overexposure
C. Motion blur
Answer with the option's letter from the given choices directly.

prompts: [["Are there any distortions in these two images?\nA. Noise\nB. Overexposure\nC. Motion blur\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5588,[Response]: A.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 655:  66%|▋| 656/1000 
[Running Accuracy]: 0.5579,[Response]: C.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 656:  66%|▋| 656/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are there any distortions in these two images?\nA. Noise\nB. Overexposure\nC. Motion blur\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how clear is the first image?
A. similar
B. less clear
C. clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how clear is the first image?
A. similar
B. less clear
C. clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how clear is the first image?\nA. similar\nB. less clear\nC. clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5579,[Response]: C.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 656:  66%|▋| 657/1000 [0
[Running Accuracy]: 0.5571,[Response]: B.<|endoftext|>, [Correct Ans]: clearer, , [Prog]: 657:  66%|▋| 657/1000 [07:16<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how clear is the first image?\nA. similar\nB. less clear\nC. clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by overexposure?
A. Front building in the second image
B. Aircraft in the first image
C. Left sky in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by overexposure?
A. Front building in the second image
B. Aircraft in the first image
C. Left sky in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by overexposure?\nA. Front building in the second image\nB. Aircraft in the first image\nC. Left sky in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5571,[Response]: B.<|endoftext|>, [Correct Ans]: clearer, , [Prog]: 657:  66%|▋| 658/1000 [07:17<
[Running Accuracy]: 0.5578,[Response]: C.<|endoftext|>, [Correct Ans]: Left sky in the second image, , [Prog]: 658:  66
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by overexposure?\nA. Front building in the second image\nB. Aircraft in the first image\nC. Left sky in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the vividness of the color in the second image?
A. more vivid
B. about the same
C. less vivid
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the vividness of the color in the second image?
A. more vivid
B. about the same
C. less vivid
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the vividness of the color in the second image?\nA. more vivid\nB. about the same\nC. less vivid\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5578,[Response]: C.<|endoftext|>, [Correct Ans]: Left sky in the second image, , [Prog]: 658:  66
[Running Accuracy]: 0.5584,[Response]: A.<|endoftext|>, [Correct Ans]: more vivid, , [Prog]: 659:  66%|▋| 659/1000 [07:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the vividness of the color in the second image?\nA. more vivid\nB. about the same\nC. less vivid\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most affected by motion blur?
A. The crimson flower in the second image
B. Text in the first image
C. Leaves in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most affected by motion blur?
A. The crimson flower in the second image
B. Text in the first image
C. Leaves in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most affected by motion blur?\nA. The crimson flower in the second image\nB. Text in the first image\nC. Leaves in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5584,[Response]: A.<|endoftext|>, [Correct Ans]: more vivid, , [Prog]: 659:  66%|▋| 660/1000 [07:
[Running Accuracy]: 0.5576,[Response]: A.<|endoftext|>, [Correct Ans]: Text in the first image, , [Prog]: 660:  66%|▋| 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most affected by motion blur?\nA. The crimson flower in the second image\nB. Text in the first image\nC. Leaves in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Both of these images do not have which kind of distortion problem?
A. overexposure
B. motion blur
C. noise
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Both of these images do not have which kind of distortion problem?
A. overexposure
B. motion blur
C. noise
Answer with the option's letter from the given choices directly.

prompts: [["Both of these images do not have which kind of distortion problem?\nA. overexposure\nB. motion blur\nC. noise\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5576,[Response]: A.<|endoftext|>, [Correct Ans]: Text in the first image, , [Prog]: 660:  66%|▋| 
[Running Accuracy]: 0.5567,[Response]: A.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 661:  66%|▋| 661/1000 [07
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Both of these images do not have which kind of distortion problem?\nA. overexposure\nB. motion blur\nC. noise\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by overexposure?
A. The wall in the first image
B. The large tree on the right side in the second image
C. The street light in the second image
D. The clothes hanger in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by overexposure?
A. The wall in the first image
B. The large tree on the right side in the second image
C. The street light in the second image
D. The clothes hanger in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by overexposure?\nA. The wall in the first image\nB. The large tree on the right side in the second image\nC. The street light in the second image\nD. The clothes hanger in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5567,[Response]: A.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 661:  66%|▋| 662/1000 [07
[Running Accuracy]: 0.5574,[Response]: C.<|endoftext|>, [Correct Ans]: The street light in the second image, , [Prog]: 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by overexposure?\nA. The wall in the first image\nB. The large tree on the right side in the second image\nC. The street light in the second image\nD. The clothes hanger in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the color richness of the second image?
A. Richer
B. More monotonous
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the color richness of the second image?
A. Richer
B. More monotonous
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the color richness of the second image?\nA. Richer\nB. More monotonous\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5574,[Response]: C.<|endoftext|>, [Correct Ans]: The street light in the second image, , [Prog]: 
[Running Accuracy]: 0.5566,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 663:  66%|▋| 663/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the color richness of the second image?\nA. Richer\nB. More monotonous\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5566,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 663:  66%|▋| 664/1000 
[Running Accuracy]: 0.5572,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 664:  66%|▋| 664/1000 [07:21<03:16
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the illumination of the second image?
A. Less sufficient
B. More sufficient
C. Similar
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the illumination of the second image?
A. Less sufficient
B. More sufficient
C. Similar
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the illumination of the second image?\nA. Less sufficient\nB. More sufficient\nC. Similar\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5572,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 664:  66%|▋| 665/1000 [07:22<03:40
[Running Accuracy]: 0.5579,[Response]: B.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 665:  66%|▋| 665/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the illumination of the second image?\nA. Less sufficient\nB. More sufficient\nC. Similar\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5579,[Response]: B.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 665:  67%|▋| 666/1000
[Running Accuracy]: 0.5571,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 666:  67%|▋| 666/1000 [07:22<03:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5571,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 666:  67%|▋| 667/1000 [07:23<03:2
[Running Accuracy]: 0.5562,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 667:  67%|▋| 667/1000 [07:23<03:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most severely affected by motion blur?
A. The white chair in the second image
B. The floor in the first image
C. The green vehicle in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most severely affected by motion blur?
A. The white chair in the second image
B. The floor in the first image
C. The green vehicle in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most severely affected by motion blur?\nA. The white chair in the second image\nB. The floor in the first image\nC. The green vehicle in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5562,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 667:  67%|▋| 668/1000 [07:24<03:5
[Running Accuracy]: 0.5569,[Response]: A.<|endoftext|>, [Correct Ans]: The white chair in the second image, , [Prog]: 6
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most severely affected by motion blur?\nA. The white chair in the second image\nB. The floor in the first image\nC. The green vehicle in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are there any distortions in these two images?
A. Out of focus
B. Overexposed
C. Underexposed
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are there any distortions in these two images?
A. Out of focus
B. Overexposed
C. Underexposed
Answer with the option's letter from the given choices directly.

prompts: [["Are there any distortions in these two images?\nA. Out of focus\nB. Overexposed\nC. Underexposed\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5569,[Response]: A.<|endoftext|>, [Correct Ans]: The white chair in the second image, , [Prog]: 6
[Running Accuracy]: 0.5561,[Response]: A.<|endoftext|>, [Correct Ans]: Underexposed, , [Prog]: 669:  67%|▋| 669/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are there any distortions in these two images?\nA. Out of focus\nB. Overexposed\nC. Underexposed\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image blurrier than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image blurrier than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image blurrier than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5561,[Response]: A.<|endoftext|>, [Correct Ans]: Underexposed, , [Prog]: 669:  67%|▋| 670/1000 [0
[Running Accuracy]: 0.5552,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 670:  67%|▋| 670/1000 [07:25<03:48
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image blurrier than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is affected by motion blur?
A. The ground in the first image
B. The dog in the first image
C. The baby in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is affected by motion blur?
A. The ground in the first image
B. The dog in the first image
C. The baby in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is affected by motion blur?\nA. The ground in the first image\nB. The dog in the first image\nC. The baby in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5552,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 670:  67%|▋| 671/1000 [07:26<03:35
[Running Accuracy]: 0.5544,[Response]: B.<|endoftext|>, [Correct Ans]: The baby in the second image, , [Prog]: 671:  67
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is affected by motion blur?\nA. The ground in the first image\nB. The dog in the first image\nC. The baby in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image more vivid than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image more vivid than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image more vivid than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5544,[Response]: B.<|endoftext|>, [Correct Ans]: The baby in the second image, , [Prog]: 671:  67
[Running Accuracy]: 0.5536,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 672:  67%|▋| 672/1000 [07:26<03:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image more vivid than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how rich are the colors in the second image?
A. Richer
B. Monotonous
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how rich are the colors in the second image?
A. Richer
B. Monotonous
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how rich are the colors in the second image?\nA. Richer\nB. Monotonous\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5536,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 672:  67%|▋| 673/1000 [07:27<03:3
[Running Accuracy]: 0.5542,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 673:  67%|▋| 673/1000 [07:27<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how rich are the colors in the second image?\nA. Richer\nB. Monotonous\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image blurrier than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image blurrier than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image blurrier than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5542,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 673:  67%|▋| 674/1000 [07:28<0
[Running Accuracy]: 0.5534,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 674:  67%|▋| 674/1000 [07:28<04:00
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image blurrier than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5534,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 674:  68%|▋| 675/1000 [07:28<03:41
[Running Accuracy]: 0.5526,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 675:  68%|▋| 675/1000 [07:28<03:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the lighting conditions weak in both of these images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the lighting conditions weak in both of these images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the lighting conditions weak in both of these images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5526,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 675:  68%|▋| 676/1000 [07:29<03:3
[Running Accuracy]: 0.5518,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 676:  68%|▋| 676/1000 [07:29<03:34
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the lighting conditions weak in both of these images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how clear is the second image?
A. About the same
B. More blurry
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how clear is the second image?
A. About the same
B. More blurry
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how clear is the second image?\nA. About the same\nB. More blurry\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5518,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 676:  68%|▋| 677/1000 [07:30<03:31
[Running Accuracy]: 0.5510,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 677:  68%|▋| 677/1000 [07
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how clear is the second image?\nA. About the same\nB. More blurry\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the texture details of these two images both rich?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the texture details of these two images both rich?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the texture details of these two images both rich?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5510,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 677:  68%|▋| 678/1000 [07
[Running Accuracy]: 0.5501,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 678:  68%|▋| 678/1000 [07:30<03:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the texture details of these two images both rich?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The sky in the second image
B. The people in the second image
C. The right side of the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The sky in the second image
B. The people in the second image
C. The right side of the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The sky in the second image\nB. The people in the second image\nC. The right side of the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5501,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 678:  68%|▋| 679/1000 [07:31<03:1
[Running Accuracy]: 0.5508,[Response]: A.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 679:  68%
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The sky in the second image\nB. The people in the second image\nC. The right side of the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how rich are the texture details in the second image?
A. almost the same
B. less rich
C. much richer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how rich are the texture details in the second image?
A. almost the same
B. less rich
C. much richer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how rich are the texture details in the second image?\nA. almost the same\nB. less rich\nC. much richer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5508,[Response]: A.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 679:  68%
[Running Accuracy]: 0.5500,[Response]: C.<|endoftext|>, [Correct Ans]: almost the same, , [Prog]: 680:  68%|▋| 680/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how rich are the texture details in the second image?\nA. almost the same\nB. less rich\nC. much richer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Have both of these images experienced motion blur?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Have both of these images experienced motion blur?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Have both of these images experienced motion blur?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5500,[Response]: C.<|endoftext|>, [Correct Ans]: almost the same, , [Prog]: 680:  68%|▋| 681/1000
[Running Accuracy]: 0.5507,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 681:  68%|▋| 681/1000 [07:32<03:12
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Have both of these images experienced motion blur?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how clear is the second image?
A. Clearer
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how clear is the second image?
A. Clearer
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how clear is the second image?\nA. Clearer\nB. Blurrier\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5507,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 681:  68%|▋| 682/1000 [07:33<03:08
[Running Accuracy]: 0.5513,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 682:  68%|▋| 682/1000 [07:33
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how clear is the second image?\nA. Clearer\nB. Blurrier\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the illumination of these two images sufficient?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the illumination of these two images sufficient?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the illumination of these two images sufficient?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5513,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 682:  68%|▋| 683/1000 [07:33
[Running Accuracy]: 0.5520,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 683:  68%|▋| 683/1000 [07:33<03:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the illumination of these two images sufficient?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below suffers from the most severe overexposure?
A. The pedestrian in the first image
B. The top left corner of the second image
C. The puppy in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below suffers from the most severe overexposure?
A. The pedestrian in the first image
B. The top left corner of the second image
C. The puppy in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below suffers from the most severe overexposure?\nA. The pedestrian in the first image\nB. The top left corner of the second image\nC. The puppy in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5520,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 683:  68%|▋| 684/1000 [07:34<03:0
[Running Accuracy]: 0.5526,[Response]: B.<|endoftext|>, [Correct Ans]: The top left corner of the second image, , [Prog
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below suffers from the most severe overexposure?\nA. The pedestrian in the first image\nB. The top left corner of the second image\nC. The puppy in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the noise level of the second image?
A. More severe
B. About the same
C. Milder
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the noise level of the second image?
A. More severe
B. About the same
C. Milder
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the noise level of the second image?\nA. More severe\nB. About the same\nC. Milder\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5526,[Response]: B.<|endoftext|>, [Correct Ans]: The top left corner of the second image, , [Prog
[Running Accuracy]: 0.5533,[Response]: A.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 685:  68%|▋| 685/1000 [07
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the noise level of the second image?\nA. More severe\nB. About the same\nC. Milder\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the illumination of the second image?
A. Similar
B. More Adequate
C. Less Adequate
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the illumination of the second image?
A. Similar
B. More Adequate
C. Less Adequate
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the illumination of the second image?\nA. Similar\nB. More Adequate\nC. Less Adequate\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5533,[Response]: A.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 685:  69%|▋| 686/1000 [07
[Running Accuracy]: 0.5525,[Response]: C.<|endoftext|>, [Correct Ans]: More Adequate, , [Prog]: 686:  69%|▋| 686/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the illumination of the second image?\nA. Similar\nB. More Adequate\nC. Less Adequate\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the texture detail of the first image richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the texture detail of the first image richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the texture detail of the first image richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5525,[Response]: C.<|endoftext|>, [Correct Ans]: More Adequate, , [Prog]: 686:  69%|▋| 687/1000 [
[Running Accuracy]: 0.5517,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 687:  69%|▋| 687/1000 [07:36<03:08
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the texture detail of the first image richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, what is the level of noise in the second image?
A. Similar
B. Slightly less
C. More severe
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, what is the level of noise in the second image?
A. Similar
B. Slightly less
C. More severe
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, what is the level of noise in the second image?\nA. Similar\nB. Slightly less\nC. More severe\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5000], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5517,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 687:  69%|▋| 688/1000 [07:36<03:06
[Running Accuracy]: 0.5523,[Response]: C.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 688:  69%|▋| 688/1000 [07
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, what is the level of noise in the second image?\nA. Similar\nB. Slightly less\nC. More severe\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image more monotonous than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image more monotonous than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image more monotonous than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5523,[Response]: C.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 688:  69%|▋| 689/1000 [07
[Running Accuracy]: 0.5515,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 689:  69%|▋| 689/1000 [07:37<03:01
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image more monotonous than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the illumination sufficient in both of these images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the illumination sufficient in both of these images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the illumination sufficient in both of these images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.6406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5515,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 689:  69%|▋| 690/1000 [07:38<03:47
[Running Accuracy]: 0.5522,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 690:  69%|▋| 690/1000 [07:38<03:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the illumination sufficient in both of these images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how would you rate the authenticity of the second image?
A. More authentic
B. About the same
C. Less authentic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how would you rate the authenticity of the second image?
A. More authentic
B. About the same
C. Less authentic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how would you rate the authenticity of the second image?\nA. More authentic\nB. About the same\nC. Less authentic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5522,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 690:  69%|▋| 691/1000 [07:38<03:2
[Running Accuracy]: 0.5528,[Response]: C.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 691:  69%|▋| 691/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how would you rate the authenticity of the second image?\nA. More authentic\nB. About the same\nC. Less authentic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5528,[Response]: C.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 691:  69%|▋| 692/1000 
[Running Accuracy]: 0.5520,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 692:  69%|▋| 692/1000 [07:39<03:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the overexposure issue in the first image more serious than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the overexposure issue in the first image more serious than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the overexposure issue in the first image more serious than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5520,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 692:  69%|▋| 693/1000 [07:40<03:1
[Running Accuracy]: 0.5527,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 693:  69%|▋| 693/1000 [07:40<03:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the overexposure issue in the first image more serious than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the lighting in the second image?
A. less sufficient
B. more sufficient
C. about the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the lighting in the second image?
A. less sufficient
B. more sufficient
C. about the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the lighting in the second image?\nA. less sufficient\nB. more sufficient\nC. about the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5527,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 693:  69%|▋| 694/1000 [07:40<03:0
[Running Accuracy]: 0.5533,[Response]: B.<|endoftext|>, [Correct Ans]: more sufficient, , [Prog]: 694:  69%|▋| 694/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the lighting in the second image?\nA. less sufficient\nB. more sufficient\nC. about the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is not affected by noise?
A. The wine glass in the first image
B. The wall in the second image
C. The toothbrush in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is not affected by noise?
A. The wine glass in the first image
B. The wall in the second image
C. The toothbrush in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is not affected by noise?\nA. The wine glass in the first image\nB. The wall in the second image\nC. The toothbrush in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5533,[Response]: B.<|endoftext|>, [Correct Ans]: more sufficient, , [Prog]: 694:  70%|▋| 695/1000
[Running Accuracy]: 0.5525,[Response]: C.<|endoftext|>, [Correct Ans]: The wine glass in the first image, , [Prog]: 695
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is not affected by noise?\nA. The wine glass in the first image\nB. The wall in the second image\nC. The toothbrush in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image more vivid than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image more vivid than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image more vivid than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5525,[Response]: C.<|endoftext|>, [Correct Ans]: The wine glass in the first image, , [Prog]: 695
[Running Accuracy]: 0.5517,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 696:  70%|▋| 696/1000 [07:41<03:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image more vivid than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>No distortion appears in both of these two images?
A. Noise
B. Motion blur
C. Out of focus
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:No distortion appears in both of these two images?
A. Noise
B. Motion blur
C. Out of focus
Answer with the option's letter from the given choices directly.

prompts: [["No distortion appears in both of these two images?\nA. Noise\nB. Motion blur\nC. Out of focus\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5517,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 696:  70%|▋| 697/1000 [07:42<02:5
[Running Accuracy]: 0.5509,[Response]: C.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 697:  70%|▋| 697/1000 [07:42<02
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>No distortion appears in both of these two images?\nA. Noise\nB. Motion blur\nC. Out of focus\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the sharpness of the first image lower than that of the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the sharpness of the first image lower than that of the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the sharpness of the first image lower than that of the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5509,[Response]: C.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 697:  70%|▋| 698/1000 [07:42<02
[Running Accuracy]: 0.5501,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 698:  70%|▋| 698/1000 [07:42<02:54
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the sharpness of the first image lower than that of the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Have both figures in these two images been overexposed?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Have both figures in these two images been overexposed?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Have both figures in these two images been overexposed?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5501,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 698:  70%|▋| 699/1000 [07:43<02:50
[Running Accuracy]: 0.5494,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 699:  70%|▋| 699/1000 [07:43<02:50
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Have both figures in these two images been overexposed?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Relative to the first image, how is the sharpness of the second image?
A. Much higher
B. About the same
C. Slightly lower
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Relative to the first image, how is the sharpness of the second image?
A. Much higher
B. About the same
C. Slightly lower
Answer with the option's letter from the given choices directly.

prompts: [["Relative to the first image, how is the sharpness of the second image?\nA. Much higher\nB. About the same\nC. Slightly lower\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5494,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 699:  70%|▋| 700/1000 [07:44<02:55
[Running Accuracy]: 0.5500,[Response]: C.<|endoftext|>, [Correct Ans]: Slightly lower, , [Prog]: 700:  70%|▋| 700/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Relative to the first image, how is the sharpness of the second image?\nA. Much higher\nB. About the same\nC. Slightly lower\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5500,[Response]: C.<|endoftext|>, [Correct Ans]: Slightly lower, , [Prog]: 700:  70%|▋| 701/1000 
[Running Accuracy]: 0.5506,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 701:  70%|▋| 701/1000 [07:44<02:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the richness of color in the second image?
A. Richer
B. More monotonous
C. Similar
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the richness of color in the second image?
A. Richer
B. More monotonous
C. Similar
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the richness of color in the second image?\nA. Richer\nB. More monotonous\nC. Similar\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5506,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 701:  70%|▋| 702/1000 [07:45<02:4
[Running Accuracy]: 0.5513,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 702:  70%|▋| 702/1000 [07:45<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the richness of color in the second image?\nA. Richer\nB. More monotonous\nC. Similar\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the sharpness of the second image?
A. Blurrier
B. Sharper
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the sharpness of the second image?
A. Blurrier
B. Sharper
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the sharpness of the second image?\nA. Blurrier\nB. Sharper\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5513,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 702:  70%|▋| 703/1000 [07:45<0
[Running Accuracy]: 0.5519,[Response]: A.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 703:  70%|▋| 703/1000 [07:45
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the sharpness of the second image?\nA. Blurrier\nB. Sharper\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the illumination of the second image?
A. More inadequate
B. More adequate
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the illumination of the second image?
A. More inadequate
B. More adequate
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the illumination of the second image?\nA. More inadequate\nB. More adequate\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5519,[Response]: A.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 703:  70%|▋| 704/1000 [07:46
[Running Accuracy]: 0.5526,[Response]: B.<|endoftext|>, [Correct Ans]: More adequate, , [Prog]: 704:  70%|▋| 704/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the illumination of the second image?\nA. More inadequate\nB. More adequate\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. Left cabinet of the first image
B. Person in the second image
C. Horse in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. Left cabinet of the first image
B. Person in the second image
C. Horse in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. Left cabinet of the first image\nB. Person in the second image\nC. Horse in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5526,[Response]: B.<|endoftext|>, [Correct Ans]: More adequate, , [Prog]: 704:  70%|▋| 705/1000 [
[Running Accuracy]: 0.5518,[Response]: B.<|endoftext|>, [Correct Ans]: Left cabinet of the first image, , [Prog]: 705: 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. Left cabinet of the first image\nB. Person in the second image\nC. Horse in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5518,[Response]: B.<|endoftext|>, [Correct Ans]: Left cabinet of the first image, , [Prog]: 705: 
[Running Accuracy]: 0.5524,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 706:  71%|▋| 706/1000 [07:47<03:00
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which kind of distortion is not present in these two images?
A. Noise
B. Motion blur
C. Ghosting
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which kind of distortion is not present in these two images?
A. Noise
B. Motion blur
C. Ghosting
Answer with the option's letter from the given choices directly.

prompts: [["Which kind of distortion is not present in these two images?\nA. Noise\nB. Motion blur\nC. Ghosting\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5524,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 706:  71%|▋| 707/1000 [07:48<03:18
[Running Accuracy]: 0.5516,[Response]: A.<|endoftext|>, [Correct Ans]: Ghosting, , [Prog]: 707:  71%|▋| 707/1000 [07:48
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which kind of distortion is not present in these two images?\nA. Noise\nB. Motion blur\nC. Ghosting\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the richness of colors in the second image?
A. More monotonous
B. More abundant
C. Similar
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the richness of colors in the second image?
A. More monotonous
B. More abundant
C. Similar
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the richness of colors in the second image?\nA. More monotonous\nB. More abundant\nC. Similar\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5516,[Response]: A.<|endoftext|>, [Correct Ans]: Ghosting, , [Prog]: 707:  71%|▋| 708/1000 [07:49
[Running Accuracy]: 0.5523,[Response]: A.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 708:  71%|▋| 708/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the richness of colors in the second image?\nA. More monotonous\nB. More abundant\nC. Similar\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the illumination sufficient in these two images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the illumination sufficient in these two images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the illumination sufficient in these two images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5523,[Response]: A.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 708:  71%|▋| 709/1000
[Running Accuracy]: 0.5529,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 709:  71%|▋| 709/1000 [07:49<02:55
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the illumination sufficient in these two images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The person in red in the second image
B. The facial part of the person in the first image
C. The sunglasses in the first image
D. The top of the tent in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The person in red in the second image
B. The facial part of the person in the first image
C. The sunglasses in the first image
D. The top of the tent in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The person in red in the second image\nB. The facial part of the person in the first image\nC. The sunglasses in the first image\nD. The top of the tent in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.5529,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 709:  71%|▋| 710/1000 [07:50<03:16
[Running Accuracy]: 0.5535,[Response]: D.<|endoftext|>, [Correct Ans]: The top of the tent in the second image, , [Prog
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The person in red in the second image\nB. The facial part of the person in the first image\nC. The sunglasses in the first image\nD. The top of the tent in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What kind of distortion did not occur in these two images?
A. Out of focus
B. Noise
C. Motion blur
D. Overexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What kind of distortion did not occur in these two images?
A. Out of focus
B. Noise
C. Motion blur
D. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion did not occur in these two images?\nA. Out of focus\nB. Noise\nC. Motion blur\nD. Overexposure\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5535,[Response]: D.<|endoftext|>, [Correct Ans]: The top of the tent in the second image, , [Prog
[Running Accuracy]: 0.5527,[Response]: A.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 711:  71%|▋| 711/1000 [07
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What kind of distortion did not occur in these two images?\nA. Out of focus\nB. Noise\nC. Motion blur\nD. Overexposure\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5527,[Response]: A.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 711:  71%|▋| 712/1000 [07
[Running Accuracy]: 0.5520,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 712:  71%|▋| 712/1000 [07:51<03:16
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the colors of these two images both rich?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the colors of these two images both rich?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the colors of these two images both rich?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5520,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 712:  71%|▋| 713/1000 [07:52<03:06
[Running Accuracy]: 0.5526,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 713:  71%|▋| 713/1000 [07:52<03:06
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the colors of these two images both rich?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Did both of these images have overexposure issues?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Did both of these images have overexposure issues?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Did both of these images have overexposure issues?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5526,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 713:  71%|▋| 714/1000 [07:53<03:02
[Running Accuracy]: 0.5532,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 714:  71%|▋| 714/1000 [07:53<03:02
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Did both of these images have overexposure issues?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the lighting in the second image?
A. more sufficient
B. less sufficient
C. about the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the lighting in the second image?
A. more sufficient
B. less sufficient
C. about the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the lighting in the second image?\nA. more sufficient\nB. less sufficient\nC. about the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-29.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5532,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 714:  72%|▋| 715/1000 [07:53<02:53
[Running Accuracy]: 0.5538,[Response]: B.<|endoftext|>, [Correct Ans]: less sufficient, , [Prog]: 715:  72%|▋| 715/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the lighting in the second image?\nA. more sufficient\nB. less sufficient\nC. about the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how realistic is the second image?
A. More realistic
B. About the same
C. Less realistic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how realistic is the second image?
A. More realistic
B. About the same
C. Less realistic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how realistic is the second image?\nA. More realistic\nB. About the same\nC. Less realistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5538,[Response]: B.<|endoftext|>, [Correct Ans]: less sufficient, , [Prog]: 715:  72%|▋| 716/1000
[Running Accuracy]: 0.5545,[Response]: C.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 716:  72%|▋| 716/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how realistic is the second image?\nA. More realistic\nB. About the same\nC. Less realistic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are there any distortion issues in these two images?
A. Motion Blur
B. Light Halo Trailing
C. Noise
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are there any distortion issues in these two images?
A. Motion Blur
B. Light Halo Trailing
C. Noise
Answer with the option's letter from the given choices directly.

prompts: [["Are there any distortion issues in these two images?\nA. Motion Blur\nB. Light Halo Trailing\nC. Noise\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5545,[Response]: C.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 716:  72%|▋| 717/1000 
[Running Accuracy]: 0.5537,[Response]: A.<|endoftext|>, [Correct Ans]: Light Halo Trailing, , [Prog]: 717:  72%|▋| 717/
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are there any distortion issues in these two images?\nA. Motion Blur\nB. Light Halo Trailing\nC. Noise\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Much blurrier
B. Much clearer
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Much blurrier
B. Much clearer
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Much blurrier\nB. Much clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5537,[Response]: A.<|endoftext|>, [Correct Ans]: Light Halo Trailing, , [Prog]: 717:  72%|▋| 718/
[Running Accuracy]: 0.5529,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 718:  72%|▋| 718/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Much blurrier\nB. Much clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the color vividness of the second image?
A. Less vivid
B. About the same
C. More vivid
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the color vividness of the second image?
A. Less vivid
B. About the same
C. More vivid
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the color vividness of the second image?\nA. Less vivid\nB. About the same\nC. More vivid\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5529,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 718:  72%|▋| 719/1000 
[Running Accuracy]: 0.5535,[Response]: C.<|endoftext|>, [Correct Ans]: More vivid, , [Prog]: 719:  72%|▋| 719/1000 [07:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the color vividness of the second image?\nA. Less vivid\nB. About the same\nC. More vivid\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image more vivid than that of the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image more vivid than that of the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image more vivid than that of the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5535,[Response]: C.<|endoftext|>, [Correct Ans]: More vivid, , [Prog]: 719:  72%|▋| 720/1000 [07:
[Running Accuracy]: 0.5542,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 720:  72%|▋| 720/1000 [07:56<02:46
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image more vivid than that of the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below has not been affected by noise?
A. The signboard in the second image
B. The beach in the first image
C. The puppy in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below has not been affected by noise?
A. The signboard in the second image
B. The beach in the first image
C. The puppy in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below has not been affected by noise?\nA. The signboard in the second image\nB. The beach in the first image\nC. The puppy in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5542,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 720:  72%|▋| 721/1000 [07:57<02:45
[Running Accuracy]: 0.5548,[Response]: A.<|endoftext|>, [Correct Ans]: The signboard in the second image, , [Prog]: 721
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below has not been affected by noise?\nA. The signboard in the second image\nB. The beach in the first image\nC. The puppy in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the color richness of the second image?
A. Similar
B. Less rich
C. Richer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the color richness of the second image?
A. Similar
B. Less rich
C. Richer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the color richness of the second image?\nA. Similar\nB. Less rich\nC. Richer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5548,[Response]: A.<|endoftext|>, [Correct Ans]: The signboard in the second image, , [Prog]: 721
[Running Accuracy]: 0.5554,[Response]: B.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 722:  72%|▋| 722/1000 [07:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the color richness of the second image?\nA. Similar\nB. Less rich\nC. Richer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared with the first image, how does the authenticity of the second image differ?
A. Similar
B. Less realistic
C. More realistic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared with the first image, how does the authenticity of the second image differ?
A. Similar
B. Less realistic
C. More realistic
Answer with the option's letter from the given choices directly.

prompts: [["Compared with the first image, how does the authenticity of the second image differ?\nA. Similar\nB. Less realistic\nC. More realistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.4844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5554,[Response]: B.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 722:  72%|▋| 723/1000 [07:5
[Running Accuracy]: 0.5560,[Response]: B.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 723:  72%|▋| 723/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared with the first image, how does the authenticity of the second image differ?\nA. Similar\nB. Less realistic\nC. More realistic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are there mosaic-like distortions in both of these two images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are there mosaic-like distortions in both of these two images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are there mosaic-like distortions in both of these two images?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5560,[Response]: B.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 723:  72%|▋| 724/1000 
[Running Accuracy]: 0.5566,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 724:  72%|▋| 724/1000 [07:59<02:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are there mosaic-like distortions in both of these two images?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images relatively true to life?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images relatively true to life?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images relatively true to life?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5566,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 724:  72%|▋| 725/1000 [07:59<02:3
[Running Accuracy]: 0.5572,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 725:  72%|▋| 725/1000 [07:59<02:39
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images relatively true to life?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by noise?
A. The horse in the first image
B. The grassland in the second image
C. The sky in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by noise?
A. The horse in the first image
B. The grassland in the second image
C. The sky in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by noise?\nA. The horse in the first image\nB. The grassland in the second image\nC. The sky in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5572,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 725:  73%|▋| 726/1000 [08:00<02:40
[Running Accuracy]: 0.5565,[Response]: B.<|endoftext|>, [Correct Ans]: The horse in the first image, , [Prog]: 726:  73
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by noise?\nA. The horse in the first image\nB. The grassland in the second image\nC. The sky in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the illumination sufficient in these two images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the illumination sufficient in these two images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the illumination sufficient in these two images?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5565,[Response]: B.<|endoftext|>, [Correct Ans]: The horse in the first image, , [Prog]: 726:  73
[Running Accuracy]: 0.5571,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 727:  73%|▋| 727/1000 [08:00<02:36
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the illumination sufficient in these two images?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most severely affected by motion blur?
A. The man in black clothing in the second image
B. The table in the first image
C. The child in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most severely affected by motion blur?
A. The man in black clothing in the second image
B. The table in the first image
C. The child in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most severely affected by motion blur?\nA. The man in black clothing in the second image\nB. The table in the first image\nC. The child in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5571,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 727:  73%|▋| 728/1000 [08:01<02:37
[Running Accuracy]: 0.5577,[Response]: A.<|endoftext|>, [Correct Ans]: The man in black clothing in the second image, ,
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most severely affected by motion blur?\nA. The man in black clothing in the second image\nB. The table in the first image\nC. The child in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Clearer
B. About the same
C. More blurred
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Clearer
B. About the same
C. More blurred
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Clearer\nB. About the same\nC. More blurred\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5577,[Response]: A.<|endoftext|>, [Correct Ans]: The man in black clothing in the second image, ,
[Running Accuracy]: 0.5583,[Response]: C.<|endoftext|>, [Correct Ans]: More blurred, , [Prog]: 729:  73%|▋| 729/1000 [0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Clearer\nB. About the same\nC. More blurred\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by motion blur?
A. waterfall in the second image
B. banana tree in the first image
C. stone in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by motion blur?
A. waterfall in the second image
B. banana tree in the first image
C. stone in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by motion blur?\nA. waterfall in the second image\nB. banana tree in the first image\nC. stone in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5583,[Response]: C.<|endoftext|>, [Correct Ans]: More blurred, , [Prog]: 729:  73%|▋| 730/1000 [0
[Running Accuracy]: 0.5589,[Response]: B.<|endoftext|>, [Correct Ans]: banana tree in the first image, , [Prog]: 730:  
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by motion blur?\nA. waterfall in the second image\nB. banana tree in the first image\nC. stone in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the colors of these two images not very vivid?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the colors of these two images not very vivid?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the colors of these two images not very vivid?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5589,[Response]: B.<|endoftext|>, [Correct Ans]: banana tree in the first image, , [Prog]: 730:  
[Running Accuracy]: 0.5595,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 731:  73%|▋| 731/1000 [08:03<02:58
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the colors of these two images not very vivid?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Clearer
B. About the same
C. More blurry
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Clearer
B. About the same
C. More blurry
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Clearer\nB. About the same\nC. More blurry\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5595,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 731:  73%|▋| 732/1000 [08:04<02:51
[Running Accuracy]: 0.5601,[Response]: A.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 732:  73%|▋| 732/1000 [08:04<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Clearer\nB. About the same\nC. More blurry\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the colors of these two images both rich?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the colors of these two images both rich?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the colors of these two images both rich?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5601,[Response]: A.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 732:  73%|▋| 733/1000 [08:04<
[Running Accuracy]: 0.5607,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 733:  73%|▋| 733/1000 [08:04<02:43
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the colors of these two images both rich?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by motion blur?
A. The golf ball in the first image
B. The left foot of the person in the first image
C. The frame in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by motion blur?
A. The golf ball in the first image
B. The left foot of the person in the first image
C. The frame in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by motion blur?\nA. The golf ball in the first image\nB. The left foot of the person in the first image\nC. The frame in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5607,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 733:  73%|▋| 734/1000 [08:05<02:38
[Running Accuracy]: 0.5613,[Response]: B.<|endoftext|>, [Correct Ans]: The left foot of the person in the first image, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by motion blur?\nA. The golf ball in the first image\nB. The left foot of the person in the first image\nC. The frame in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by noise?
A. Background of the second image
B. Bus in the first image
C. People in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by noise?
A. Background of the second image
B. Bus in the first image
C. People in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by noise?\nA. Background of the second image\nB. Bus in the first image\nC. People in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5613,[Response]: B.<|endoftext|>, [Correct Ans]: The left foot of the person in the first image, 
[Running Accuracy]: 0.5605,[Response]: A.<|endoftext|>, [Correct Ans]: Bus in the first image, , [Prog]: 735:  74%|▋| 7
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by noise?\nA. Background of the second image\nB. Bus in the first image\nC. People in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the colors of these two images both vivid?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the colors of these two images both vivid?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the colors of these two images both vivid?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5605,[Response]: A.<|endoftext|>, [Correct Ans]: Bus in the first image, , [Prog]: 735:  74%|▋| 7
[Running Accuracy]: 0.5611,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 736:  74%|▋| 736/1000 [08:06<02:32
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the colors of these two images both vivid?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the color richness of the second image?
A. Richer
B. More monotonous
C. Similar
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the color richness of the second image?
A. Richer
B. More monotonous
C. Similar
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the color richness of the second image?\nA. Richer\nB. More monotonous\nC. Similar\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5611,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 736:  74%|▋| 737/1000 [08:06<02:34
[Running Accuracy]: 0.5604,[Response]: B.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 737:  74%|▋| 737/1000 [08:06<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the color richness of the second image?\nA. Richer\nB. More monotonous\nC. Similar\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. More blurry
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. More blurry
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5604,[Response]: B.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 737:  74%|▋| 738/1000 [08:07<0
[Running Accuracy]: 0.5610,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 738:  74%|▋| 738/1000 [08:07<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how does the texture detail level of the second image look like?
A. Richer
B. About the same
C. Less rich
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how does the texture detail level of the second image look like?
A. Richer
B. About the same
C. Less rich
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how does the texture detail level of the second image look like?\nA. Richer\nB. About the same\nC. Less rich\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5610,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 738:  74%|▋| 739/1000 [08:08<
[Running Accuracy]: 0.5602,[Response]: C.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 739:  74%|▋| 739/1000 [08:08<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how does the texture detail level of the second image look like?\nA. Richer\nB. About the same\nC. Less rich\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5602,[Response]: C.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 739:  74%|▋| 740/1000 [08:08<0
[Running Accuracy]: 0.5608,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 740:  74%|▋| 740/1000 [08:08<02:31
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very realistic?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part has the most severe overexposure issue?
A. The light in the second image
B. The ground in the first image
C. The ground in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part has the most severe overexposure issue?
A. The light in the second image
B. The ground in the first image
C. The ground in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the most severe overexposure issue?\nA. The light in the second image\nB. The ground in the first image\nC. The ground in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5608,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 740:  74%|▋| 741/1000 [08:09<02:26
[Running Accuracy]: 0.5614,[Response]: A.<|endoftext|>, [Correct Ans]: The light in the second image, , [Prog]: 741:  7
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part has the most severe overexposure issue?\nA. The light in the second image\nB. The ground in the first image\nC. The ground in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Clearer
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Clearer
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Clearer\nB. Blurrier\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5614,[Response]: A.<|endoftext|>, [Correct Ans]: The light in the second image, , [Prog]: 741:  7
[Running Accuracy]: 0.5606,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 742:  74%|▋| 742/1000 [08:09<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Clearer\nB. Blurrier\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Blurrier
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Blurrier
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Blurrier\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5606,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 742:  74%|▋| 743/1000 [08:10<
[Running Accuracy]: 0.5612,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 743:  74%|▋| 743/1000 [08:10<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Blurrier\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the illumination of the first image much more sufficient than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the illumination of the first image much more sufficient than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the illumination of the first image much more sufficient than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5612,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 743:  74%|▋| 744/1000 [08:10<
[Running Accuracy]: 0.5605,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 744:  74%|▋| 744/1000 [08:10<02:29
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the illumination of the first image much more sufficient than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how severe is the motion blur in the second image?
A. Similar
B. More severe
C. Slightly more
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how severe is the motion blur in the second image?
A. Similar
B. More severe
C. Slightly more
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how severe is the motion blur in the second image?\nA. Similar\nB. More severe\nC. Slightly more\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5605,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 744:  74%|▋| 745/1000 [08:11<02:28
[Running Accuracy]: 0.5611,[Response]: B.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 745:  74%|▋| 745/1000 [08
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how severe is the motion blur in the second image?\nA. Similar\nB. More severe\nC. Slightly more\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the texture detail of the second image?
A. Similar
B. Richer
C. Less rich
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the texture detail of the second image?
A. Similar
B. Richer
C. Less rich
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the texture detail of the second image?\nA. Similar\nB. Richer\nC. Less rich\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5611,[Response]: B.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 745:  75%|▋| 746/1000 [08
[Running Accuracy]: 0.5603,[Response]: C.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 746:  75%|▋| 746/1000 [08:12<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the texture detail of the second image?\nA. Similar\nB. Richer\nC. Less rich\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5603,[Response]: C.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 746:  75%|▋| 747/1000 [08:12<0
[Running Accuracy]: 0.5596,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 747:  75%|▋| 747/1000 [08:12<02:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the texture detail in the second image?
A. richer
B. less rich
C. about the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the texture detail in the second image?
A. richer
B. less rich
C. about the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the texture detail in the second image?\nA. richer\nB. less rich\nC. about the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5596,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 747:  75%|▋| 748/1000 [08:13<02:5
[Running Accuracy]: 0.5602,[Response]: B.<|endoftext|>, [Correct Ans]: less rich, , [Prog]: 748:  75%|▋| 748/1000 [08:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the texture detail in the second image?\nA. richer\nB. less rich\nC. about the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the illumination of the second image?
A. Less sufficient
B. More sufficient
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the illumination of the second image?
A. Less sufficient
B. More sufficient
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the illumination of the second image?\nA. Less sufficient\nB. More sufficient\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5602,[Response]: B.<|endoftext|>, [Correct Ans]: less rich, , [Prog]: 748:  75%|▋| 749/1000 [08:1
[Running Accuracy]: 0.5607,[Response]: A.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 749:  75%|▋| 749/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the illumination of the second image?\nA. Less sufficient\nB. More sufficient\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The sky in the second image
B. The bus in the second image
C. The bus in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The sky in the second image
B. The bus in the second image
C. The bus in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The sky in the second image\nB. The bus in the second image\nC. The bus in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5607,[Response]: A.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 749:  75%|▊| 750/1000
[Running Accuracy]: 0.5613,[Response]: A.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 750:  75%
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The sky in the second image\nB. The bus in the second image\nC. The bus in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. Ground in the first image
B. Sky in the second image
C. Lion in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. Ground in the first image
B. Sky in the second image
C. Lion in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. Ground in the first image\nB. Sky in the second image\nC. Lion in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5613,[Response]: A.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 750:  75%
[Running Accuracy]: 0.5619,[Response]: B.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 751:  75%|▊| 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. Ground in the first image\nB. Sky in the second image\nC. Lion in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by noise?
A. The ground in the first image
B. The person in the second image
C. The animal in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by noise?
A. The ground in the first image
B. The person in the second image
C. The animal in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by noise?\nA. The ground in the first image\nB. The person in the second image\nC. The animal in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5619,[Response]: B.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 751:  75%|▊| 
[Running Accuracy]: 0.5625,[Response]: B.<|endoftext|>, [Correct Ans]: The person in the second image, , [Prog]: 752:  
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by noise?\nA. The ground in the first image\nB. The person in the second image\nC. The animal in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how real is the second image?
A. About the same
B. More real
C. Less real
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how real is the second image?
A. About the same
B. More real
C. Less real
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how real is the second image?\nA. About the same\nB. More real\nC. Less real\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5625,[Response]: B.<|endoftext|>, [Correct Ans]: The person in the second image, , [Prog]: 752:  
[Running Accuracy]: 0.5618,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 753:  75%|▊| 753/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how real is the second image?\nA. About the same\nB. More real\nC. Less real\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the lighting of the first image more sufficient than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the lighting of the first image more sufficient than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the lighting of the first image more sufficient than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5618,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 753:  75%|▊| 754/1000 
[Running Accuracy]: 0.5610,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 754:  75%|▊| 754/1000 [08:17<02:23
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the lighting of the first image more sufficient than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5610,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 754:  76%|▊| 755/1000 [08:17<02:20
[Running Accuracy]: 0.5603,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 755:  76%|▊| 755/1000 [08:17<02:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is least affected by overexposure?
A. The sky in the second image
B. The light source in the first image
C. The person in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is least affected by overexposure?
A. The sky in the second image
B. The light source in the first image
C. The person in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is least affected by overexposure?\nA. The sky in the second image\nB. The light source in the first image\nC. The person in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5603,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 755:  76%|▊| 756/1000 [08:18<02:1
[Running Accuracy]: 0.5608,[Response]: C.<|endoftext|>, [Correct Ans]: The person in the second image, , [Prog]: 756:  
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is least affected by overexposure?\nA. The sky in the second image\nB. The light source in the first image\nC. The person in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by motion blur?
A. The vegetation in the second image
B. The kitten in the first image
C. The tabletop in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by motion blur?
A. The vegetation in the second image
B. The kitten in the first image
C. The tabletop in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by motion blur?\nA. The vegetation in the second image\nB. The kitten in the first image\nC. The tabletop in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5608,[Response]: C.<|endoftext|>, [Correct Ans]: The person in the second image, , [Prog]: 756:  
[Running Accuracy]: 0.5601,[Response]: B.<|endoftext|>, [Correct Ans]: The vegetation in the second image, , [Prog]: 75
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by motion blur?\nA. The vegetation in the second image\nB. The kitten in the first image\nC. The tabletop in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5601,[Response]: B.<|endoftext|>, [Correct Ans]: The vegetation in the second image, , [Prog]: 75
[Running Accuracy]: 0.5607,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 758:  76%|▊| 758/1000 [08:19<02:15
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5607,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 758:  76%|▊| 759/1000 [08:19<02:15
[Running Accuracy]: 0.5599,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 759:  76%|▊| 759/1000 [08:19<02:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5599,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 759:  76%|▊| 760/1000 [08:20<02:1
[Running Accuracy]: 0.5605,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 760:  76%|▊| 760/1000 [08:20<02:14
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how rich is the color in the second image?
A. richer
B. about the same
C. more monotonous
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how rich is the color in the second image?
A. richer
B. about the same
C. more monotonous
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how rich is the color in the second image?\nA. richer\nB. about the same\nC. more monotonous\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5605,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 760:  76%|▊| 761/1000 [08:20<02:16
[Running Accuracy]: 0.5611,[Response]: A.<|endoftext|>, [Correct Ans]: richer, , [Prog]: 761:  76%|▊| 761/1000 [08:20<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how rich is the color in the second image?\nA. richer\nB. about the same\nC. more monotonous\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Clearer
B. About the same
C. Blurrier
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Clearer
B. About the same
C. Blurrier
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Clearer\nB. About the same\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5611,[Response]: A.<|endoftext|>, [Correct Ans]: richer, , [Prog]: 761:  76%|▊| 762/1000 [08:21<0
[Running Accuracy]: 0.5617,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 762:  76%|▊| 762/1000 [08:21
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Clearer\nB. About the same\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. Sky in the second image
B. Left side table in the first image
C. Mountain in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. Sky in the second image
B. Left side table in the first image
C. Mountain in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. Sky in the second image\nB. Left side table in the first image\nC. Mountain in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5617,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 762:  76%|▊| 763/1000 [08:22
[Running Accuracy]: 0.5623,[Response]: A.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 763:  76%|▊| 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. Sky in the second image\nB. Left side table in the first image\nC. Mountain in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What kind of distortion issue do these two images not have?
A. Motion blur
B. Noise
C. Overexposure
D. Ghosting
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What kind of distortion issue do these two images not have?
A. Motion blur
B. Noise
C. Overexposure
D. Ghosting
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion issue do these two images not have?\nA. Motion blur\nB. Noise\nC. Overexposure\nD. Ghosting\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.5623,[Response]: A.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 763:  76%|▊| 
[Running Accuracy]: 0.5615,[Response]: D.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 764:  76%|▊| 764/1000 [08:22<02
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What kind of distortion issue do these two images not have?\nA. Motion blur\nB. Noise\nC. Overexposure\nD. Ghosting\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the sharpness of the first image lower than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the sharpness of the first image lower than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the sharpness of the first image lower than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5615,[Response]: D.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 764:  76%|▊| 765/1000 [08:23<02
[Running Accuracy]: 0.5621,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 765:  76%|▊| 765/1000 [08:23<02:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the sharpness of the first image lower than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. Window in the first image
B. Toothbrush in the second image
C. Hand in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. Window in the first image
B. Toothbrush in the second image
C. Hand in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. Window in the first image\nB. Toothbrush in the second image\nC. Hand in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5621,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 765:  77%|▊| 766/1000 [08:24<02:2
[Running Accuracy]: 0.5627,[Response]: A.<|endoftext|>, [Correct Ans]: Window in the first image, , [Prog]: 766:  77%|▊
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. Window in the first image\nB. Toothbrush in the second image\nC. Hand in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the sharpness of the second image?
A. More blurry
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the sharpness of the second image?
A. More blurry
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the sharpness of the second image?\nA. More blurry\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5627,[Response]: A.<|endoftext|>, [Correct Ans]: Window in the first image, , [Prog]: 766:  77%|▊
[Running Accuracy]: 0.5619,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 767:  77%|▊| 767/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the sharpness of the second image?\nA. More blurry\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image more monotonous than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image more monotonous than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image more monotonous than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5619,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 767:  77%|▊| 768/1000 
[Running Accuracy]: 0.5625,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 768:  77%|▊| 768/1000 [08:25<02:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image more monotonous than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image blurrier than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image blurrier than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image blurrier than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5625,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 768:  77%|▊| 769/1000 [08:26<02:2
[Running Accuracy]: 0.5618,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 769:  77%|▊| 769/1000 [08:26<02:24
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image blurrier than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the sharpness of the second image compare to the first image?
A. Blurrier
B. About the same
C. Sharper
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the sharpness of the second image compare to the first image?
A. Blurrier
B. About the same
C. Sharper
Answer with the option's letter from the given choices directly.

prompts: [["How does the sharpness of the second image compare to the first image?\nA. Blurrier\nB. About the same\nC. Sharper\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5618,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 769:  77%|▊| 770/1000 [08:26<02:20
[Running Accuracy]: 0.5610,[Response]: A.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 770:  77%|▊| 770/1000 [08:26<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the sharpness of the second image compare to the first image?\nA. Blurrier\nB. About the same\nC. Sharper\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5610,[Response]: A.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 770:  77%|▊| 771/1000 [08:27<
[Running Accuracy]: 0.5616,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 771:  77%|▊| 771/1000 [08:27<02:12
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the authenticity of these two images both very high?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the authenticity of these two images both very high?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the authenticity of these two images both very high?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5616,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 771:  77%|▊| 772/1000 [08:27<02:29
[Running Accuracy]: 0.5622,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 772:  77%|▊| 772/1000 [08:27<02:29
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the authenticity of these two images both very high?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images relatively clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images relatively clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images relatively clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5622,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 772:  77%|▊| 773/1000 [08:28<02:20
[Running Accuracy]: 0.5627,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 773:  77%|▊| 773/1000 [08:28<02:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images relatively clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the illumination of the second image compare to the first image?
A. Less sufficient
B. More sufficient
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the illumination of the second image compare to the first image?
A. Less sufficient
B. More sufficient
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["How does the illumination of the second image compare to the first image?\nA. Less sufficient\nB. More sufficient\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5627,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 773:  77%|▊| 774/1000 [08:29<02:1
[Running Accuracy]: 0.5633,[Response]: A.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 774:  77%|▊| 774/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the illumination of the second image compare to the first image?\nA. Less sufficient\nB. More sufficient\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Relative to the first image, how is the clarity of the second image?
A. Blurrier
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Relative to the first image, how is the clarity of the second image?
A. Blurrier
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Relative to the first image, how is the clarity of the second image?\nA. Blurrier\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5633,[Response]: A.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 774:  78%|▊| 775/1000
[Running Accuracy]: 0.5639,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 775:  78%|▊| 775/1000 [08:29<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Relative to the first image, how is the clarity of the second image?\nA. Blurrier\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The horse in the second image
B. The cow in the first image
C. The sky in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The horse in the second image
B. The cow in the first image
C. The sky in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The horse in the second image\nB. The cow in the first image\nC. The sky in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5639,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 775:  78%|▊| 776/1000 [08:30<
[Running Accuracy]: 0.5644,[Response]: C.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 776:  78%
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The horse in the second image\nB. The cow in the first image\nC. The sky in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very realistic?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very realistic?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very realistic?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5644,[Response]: C.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 776:  78%
[Running Accuracy]: 0.5650,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 777:  78%|▊| 777/1000 [08:30<02:11
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very realistic?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What kind of distortion exists in both of these images?
A. overexposure
B. motion blur
C. noise
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What kind of distortion exists in both of these images?
A. overexposure
B. motion blur
C. noise
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion exists in both of these images?\nA. overexposure\nB. motion blur\nC. noise\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5650,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 777:  78%|▊| 778/1000 [08:31<02:06
[Running Accuracy]: 0.5656,[Response]: C.<|endoftext|>, [Correct Ans]: noise, , [Prog]: 778:  78%|▊| 778/1000 [08:31<02
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What kind of distortion exists in both of these images?\nA. overexposure\nB. motion blur\nC. noise\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the texture detail in the second image?
A. Similar
B. More abundant
C. Less abundant
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the texture detail in the second image?
A. Similar
B. More abundant
C. Less abundant
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the texture detail in the second image?\nA. Similar\nB. More abundant\nC. Less abundant\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5656,[Response]: C.<|endoftext|>, [Correct Ans]: noise, , [Prog]: 778:  78%|▊| 779/1000 [08:31<02
[Running Accuracy]: 0.5661,[Response]: C.<|endoftext|>, [Correct Ans]: Less abundant, , [Prog]: 779:  78%|▊| 779/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the texture detail in the second image?\nA. Similar\nB. More abundant\nC. Less abundant\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5661,[Response]: C.<|endoftext|>, [Correct Ans]: Less abundant, , [Prog]: 779:  78%|▊| 780/1000 [
[Running Accuracy]: 0.5654,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 780:  78%|▊| 780/1000 [08:32<02:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the sharpness of the second image?
A. More blurry
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the sharpness of the second image?
A. More blurry
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the sharpness of the second image?\nA. More blurry\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5654,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 780:  78%|▊| 781/1000 [08:33<02:1
[Running Accuracy]: 0.5659,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 781:  78%|▊| 781/1000 [08
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the sharpness of the second image?\nA. More blurry\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. Branches in the first image
B. Base in the second image
C. Statue in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. Branches in the first image
B. Base in the second image
C. Statue in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. Branches in the first image\nB. Base in the second image\nC. Statue in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5659,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 781:  78%|▊| 782/1000 [08
[Running Accuracy]: 0.5665,[Response]: B.<|endoftext|>, [Correct Ans]: Base in the second image, , [Prog]: 782:  78%|▊|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. Branches in the first image\nB. Base in the second image\nC. Statue in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part has the most severe issue of losing texture details?
A. Checkerboard ground in the first image
B. Horse in the second image
C. Background in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part has the most severe issue of losing texture details?
A. Checkerboard ground in the first image
B. Horse in the second image
C. Background in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the most severe issue of losing texture details?\nA. Checkerboard ground in the first image\nB. Horse in the second image\nC. Background in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5665,[Response]: B.<|endoftext|>, [Correct Ans]: Base in the second image, , [Prog]: 782:  78%|▊|
[Running Accuracy]: 0.5658,[Response]: B.<|endoftext|>, [Correct Ans]: Background in the second image, , [Prog]: 783:  
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part has the most severe issue of losing texture details?\nA. Checkerboard ground in the first image\nB. Horse in the second image\nC. Background in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are there any distortion issues in these two images?
A. Lens flare
B. Motion blur
C. Underexposure
D. Overexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are there any distortion issues in these two images?
A. Lens flare
B. Motion blur
C. Underexposure
D. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["Are there any distortion issues in these two images?\nA. Lens flare\nB. Motion blur\nC. Underexposure\nD. Overexposure\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5658,[Response]: B.<|endoftext|>, [Correct Ans]: Background in the second image, , [Prog]: 783:  
[Running Accuracy]: 0.5651,[Response]: C.<|endoftext|>, [Correct Ans]: Lens flare, , [Prog]: 784:  78%|▊| 784/1000 [08:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are there any distortion issues in these two images?\nA. Lens flare\nB. Motion blur\nC. Underexposure\nD. Overexposure\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the sharpness of these two images both poor?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the sharpness of these two images both poor?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the sharpness of these two images both poor?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5651,[Response]: C.<|endoftext|>, [Correct Ans]: Lens flare, , [Prog]: 784:  78%|▊| 785/1000 [08:
[Running Accuracy]: 0.5656,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 785:  78%|▊| 785/1000 [08:35<02:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the sharpness of these two images both poor?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by noise?
A. The sky in the first image
B. The person in the first image
C. The bus in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by noise?
A. The sky in the first image
B. The person in the first image
C. The bus in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by noise?\nA. The sky in the first image\nB. The person in the first image\nC. The bus in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C

[Running Accuracy]: 0.5656,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 785:  79%|▊| 786/1000 [08:35<01:5
[Running Accuracy]: 0.5662,[Response]: C<|endoftext|>, [Correct Ans]: The bus in the second image, , [Prog]: 786:  79%|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by noise?\nA. The sky in the first image\nB. The person in the first image\nC. The bus in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the authenticity of these two images both relatively high?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the authenticity of these two images both relatively high?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the authenticity of these two images both relatively high?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5662,[Response]: C<|endoftext|>, [Correct Ans]: The bus in the second image, , [Prog]: 786:  79%|
[Running Accuracy]: 0.5654,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 787:  79%|▊| 787/1000 [08:36<01:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the authenticity of these two images both relatively high?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The door in the second image
B. The wall in the second image
C. The lamp in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The door in the second image
B. The wall in the second image
C. The lamp in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The door in the second image\nB. The wall in the second image\nC. The lamp in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5654,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 787:  79%|▊| 788/1000 [08:37<01:5
[Running Accuracy]: 0.5660,[Response]: C.<|endoftext|>, [Correct Ans]: The lamp in the first image, , [Prog]: 788:  79%
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The door in the second image\nB. The wall in the second image\nC. The lamp in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. Window in the second image
B. Aircraft in the first image
C. Person in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. Window in the second image
B. Aircraft in the first image
C. Person in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. Window in the second image\nB. Aircraft in the first image\nC. Person in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5000], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5660,[Response]: C.<|endoftext|>, [Correct Ans]: The lamp in the first image, , [Prog]: 788:  79%
[Running Accuracy]: 0.5653,[Response]: C.<|endoftext|>, [Correct Ans]: Window in the second image, , [Prog]: 789:  79%|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. Window in the second image\nB. Aircraft in the first image\nC. Person in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Clearer
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Clearer
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Clearer\nB. Blurrier\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5653,[Response]: C.<|endoftext|>, [Correct Ans]: Window in the second image, , [Prog]: 789:  79%|
[Running Accuracy]: 0.5646,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 790:  79%|▊| 790/1000 [08:38<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Clearer\nB. Blurrier\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the authenticity of these two images both high?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the authenticity of these two images both high?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the authenticity of these two images both high?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5646,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 790:  79%|▊| 791/1000 [08:38<
[Running Accuracy]: 0.5651,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 791:  79%|▊| 791/1000 [08:38<01:58
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the authenticity of these two images both high?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What kind of distortion issue do these two images not have?
A. Motion blur
B. Overexposure
C. Out of focus
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What kind of distortion issue do these two images not have?
A. Motion blur
B. Overexposure
C. Out of focus
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion issue do these two images not have?\nA. Motion blur\nB. Overexposure\nC. Out of focus\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5651,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 791:  79%|▊| 792/1000 [08:39<01:55
[Running Accuracy]: 0.5644,[Response]: C.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 792:  79%|▊| 792/1000 [08
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What kind of distortion issue do these two images not have?\nA. Motion blur\nB. Overexposure\nC. Out of focus\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the blurriness of the second image?
A. Similar
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the blurriness of the second image?
A. Similar
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the blurriness of the second image?\nA. Similar\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5644,[Response]: C.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 792:  79%|▊| 793/1000 [08
[Running Accuracy]: 0.5649,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 793:  79%|▊| 793/1000 [08:39
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the blurriness of the second image?\nA. Similar\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the texture detail of the second image?
A. More rich
B. Less rich
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the texture detail of the second image?
A. More rich
B. Less rich
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the texture detail of the second image?\nA. More rich\nB. Less rich\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5649,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 793:  79%|▊| 794/1000 [08:40
[Running Accuracy]: 0.5655,[Response]: A.<|endoftext|>, [Correct Ans]: More rich, , [Prog]: 794:  79%|▊| 794/1000 [08:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the texture detail of the second image?\nA. More rich\nB. Less rich\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The sky in the second image
B. The person in the second image
C. The strawberry in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The sky in the second image
B. The person in the second image
C. The strawberry in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The sky in the second image\nB. The person in the second image\nC. The strawberry in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5655,[Response]: A.<|endoftext|>, [Correct Ans]: More rich, , [Prog]: 794:  80%|▊| 795/1000 [08:4
[Running Accuracy]: 0.5660,[Response]: A.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 795:  80%
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The sky in the second image\nB. The person in the second image\nC. The strawberry in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the realism of the second image?
A. similar
B. more realistic
C. less realistic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the realism of the second image?
A. similar
B. more realistic
C. less realistic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the realism of the second image?\nA. similar\nB. more realistic\nC. less realistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5660,[Response]: A.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 795:  80%
[Running Accuracy]: 0.5653,[Response]: A.<|endoftext|>, [Correct Ans]: more realistic, , [Prog]: 796:  80%|▊| 796/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the realism of the second image?\nA. similar\nB. more realistic\nC. less realistic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. the sky in the first image
B. the bus in the second image
C. the sky in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. the sky in the first image
B. the bus in the second image
C. the sky in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. the sky in the first image\nB. the bus in the second image\nC. the sky in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5653,[Response]: A.<|endoftext|>, [Correct Ans]: more realistic, , [Prog]: 796:  80%|▊| 797/1000 
[Running Accuracy]: 0.5659,[Response]: A.<|endoftext|>, [Correct Ans]: the sky in the first image, , [Prog]: 797:  80%|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. the sky in the first image\nB. the bus in the second image\nC. the sky in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the texture detail of the second image?
A. Similar
B. Richer
C. Less rich
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the texture detail of the second image?
A. Similar
B. Richer
C. Less rich
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the texture detail of the second image?\nA. Similar\nB. Richer\nC. Less rich\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5659,[Response]: A.<|endoftext|>, [Correct Ans]: the sky in the first image, , [Prog]: 797:  80%|
[Running Accuracy]: 0.5664,[Response]: B.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 798:  80%|▊| 798/1000 [08:42<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the texture detail of the second image?\nA. Similar\nB. Richer\nC. Less rich\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images overexposed?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images overexposed?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images overexposed?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5664,[Response]: B.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 798:  80%|▊| 799/1000 [08:43<0
[Running Accuracy]: 0.5670,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 799:  80%|▊| 799/1000 [08:43<01:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images overexposed?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the colors of these two images both relatively vibrant?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the colors of these two images both relatively vibrant?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the colors of these two images both relatively vibrant?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5670,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 799:  80%|▊| 800/1000 [08:43<01:5
[Running Accuracy]: 0.5675,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 800:  80%|▊| 800/1000 [08:43<01:56
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the colors of these two images both relatively vibrant?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by noise?
A. The runway in the second image
B. The person in the first image
C. The person in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by noise?
A. The runway in the second image
B. The person in the first image
C. The person in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by noise?\nA. The runway in the second image\nB. The person in the first image\nC. The person in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5675,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 800:  80%|▊| 801/1000 [08:44<01:54
[Running Accuracy]: 0.5680,[Response]: B.<|endoftext|>, [Correct Ans]: The person in the first image, , [Prog]: 801:  8
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by noise?\nA. The runway in the second image\nB. The person in the first image\nC. The person in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the illumination of the second image?
A. similar
B. less sufficient
C. more sufficient
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the illumination of the second image?
A. similar
B. less sufficient
C. more sufficient
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the illumination of the second image?\nA. similar\nB. less sufficient\nC. more sufficient\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5680,[Response]: B.<|endoftext|>, [Correct Ans]: The person in the first image, , [Prog]: 801:  8
[Running Accuracy]: 0.5673,[Response]: C.<|endoftext|>, [Correct Ans]: less sufficient, , [Prog]: 802:  80%|▊| 802/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the illumination of the second image?\nA. similar\nB. less sufficient\nC. more sufficient\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images not high in sharpness?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images not high in sharpness?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images not high in sharpness?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5673,[Response]: C.<|endoftext|>, [Correct Ans]: less sufficient, , [Prog]: 802:  80%|▊| 803/1000
[Running Accuracy]: 0.5679,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 803:  80%|▊| 803/1000 [08:45<01:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images not high in sharpness?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What kind of distortion issue is not present in these two images?
A. overexposure
B. noise
C. motion blur
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What kind of distortion issue is not present in these two images?
A. overexposure
B. noise
C. motion blur
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion issue is not present in these two images?\nA. overexposure\nB. noise\nC. motion blur\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5679,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 803:  80%|▊| 804/1000 [08:46<02:0
[Running Accuracy]: 0.5672,[Response]: A.<|endoftext|>, [Correct Ans]: noise, , [Prog]: 804:  80%|▊| 804/1000 [08:46<02
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What kind of distortion issue is not present in these two images?\nA. overexposure\nB. noise\nC. motion blur\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the sharpness of the second image?
A. Similar
B. Blurrier
C. Sharper
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the sharpness of the second image?
A. Similar
B. Blurrier
C. Sharper
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the sharpness of the second image?\nA. Similar\nB. Blurrier\nC. Sharper\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5672,[Response]: A.<|endoftext|>, [Correct Ans]: noise, , [Prog]: 804:  80%|▊| 805/1000 [08:47<02
[Running Accuracy]: 0.5665,[Response]: B.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 805:  80%|▊| 805/1000 [08:47<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the sharpness of the second image?\nA. Similar\nB. Blurrier\nC. Sharper\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. More blurry
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. More blurry
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5665,[Response]: B.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 805:  81%|▊| 806/1000 [08:47<
[Running Accuracy]: 0.5670,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 806:  81%|▊| 806/1000 [08:47<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. More blurry\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Blurrier
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Blurrier
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Blurrier\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5670,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 806:  81%|▊| 807/1000 [08:48<
[Running Accuracy]: 0.5675,[Response]: A.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 807:  81%|▊| 807/1000 [08:48
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Blurrier\nB. Clearer\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the illumination of these two images sufficient?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the illumination of these two images sufficient?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the illumination of these two images sufficient?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5675,[Response]: A.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 807:  81%|▊| 808/1000 [08:48
[Running Accuracy]: 0.5668,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 808:  81%|▊| 808/1000 [08:48<01:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the illumination of these two images sufficient?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the lighting of the second image compare to the first image?
A. More sufficient
B. About the same
C. Less sufficient
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the lighting of the second image compare to the first image?
A. More sufficient
B. About the same
C. Less sufficient
Answer with the option's letter from the given choices directly.

prompts: [["How does the lighting of the second image compare to the first image?\nA. More sufficient\nB. About the same\nC. Less sufficient\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5668,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 808:  81%|▊| 809/1000 [08:49<01:5
[Running Accuracy]: 0.5674,[Response]: C.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 809:  81%|▊| 809/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the lighting of the second image compare to the first image?\nA. More sufficient\nB. About the same\nC. Less sufficient\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the colors in these two images both rich?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the colors in these two images both rich?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the colors in these two images both rich?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5674,[Response]: C.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 809:  81%|▊| 810/1000
[Running Accuracy]: 0.5679,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 810:  81%|▊| 810/1000 [08:49<01:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the colors in these two images both rich?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the color of the second image?
A. More abundant
B. More monotonous
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the color of the second image?
A. More abundant
B. More monotonous
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the color of the second image?\nA. More abundant\nB. More monotonous\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-29.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5679,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 810:  81%|▊| 811/1000 [08:50<02:0
[Running Accuracy]: 0.5684,[Response]: B.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 811:  81%|▊| 811/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the color of the second image?\nA. More abundant\nB. More monotonous\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the color of the second image?
A. Less rich
B. Similar
C. More rich
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the color of the second image?
A. Less rich
B. Similar
C. More rich
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the color of the second image?\nA. Less rich\nB. Similar\nC. More rich\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5684,[Response]: B.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 811:  81%|▊| 812/1000
[Running Accuracy]: 0.5690,[Response]: A.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 812:  81%|▊| 812/1000 [08:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the color of the second image?\nA. Less rich\nB. Similar\nC. More rich\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the lighting conditions sufficient in these two images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the lighting conditions sufficient in these two images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the lighting conditions sufficient in these two images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5690,[Response]: A.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 812:  81%|▊| 813/1000 [08:5
[Running Accuracy]: 0.5695,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 813:  81%|▊| 813/1000 [08:51<01:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the lighting conditions sufficient in these two images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the color vividness of the second image?
A. More vivid
B. Less vivid
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the color vividness of the second image?
A. More vivid
B. Less vivid
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the color vividness of the second image?\nA. More vivid\nB. Less vivid\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5695,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 813:  81%|▊| 814/1000 [08:52<01:4
[Running Accuracy]: 0.5688,[Response]: A.<|endoftext|>, [Correct Ans]: Less vivid, , [Prog]: 814:  81%|▊| 814/1000 [08:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the color vividness of the second image?\nA. More vivid\nB. Less vivid\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which kind of distortion issue is not present in these two images?
A. Out of focus
B. Motion blur
C. Overexposure
D. Noise
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which kind of distortion issue is not present in these two images?
A. Out of focus
B. Motion blur
C. Overexposure
D. Noise
Answer with the option's letter from the given choices directly.

prompts: [["Which kind of distortion issue is not present in these two images?\nA. Out of focus\nB. Motion blur\nC. Overexposure\nD. Noise\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5688,[Response]: A.<|endoftext|>, [Correct Ans]: Less vivid, , [Prog]: 814:  82%|▊| 815/1000 [08:
[Running Accuracy]: 0.5681,[Response]: C.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 815:  82%|▊| 815/1000 [08:52<01
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which kind of distortion issue is not present in these two images?\nA. Out of focus\nB. Motion blur\nC. Overexposure\nD. Noise\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the sharpness of the second image?
A. More blurry
B. About the same
C. Sharper
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the sharpness of the second image?
A. More blurry
B. About the same
C. Sharper
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the sharpness of the second image?\nA. More blurry\nB. About the same\nC. Sharper\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5681,[Response]: C.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 815:  82%|▊| 816/1000 [08:53<02
[Running Accuracy]: 0.5674,[Response]: A.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 816:  82%|▊| 816/1000 [08:53<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the sharpness of the second image?\nA. More blurry\nB. About the same\nC. Sharper\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the color richness of the second image?
A. Similar
B. Less rich
C. More rich
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the color richness of the second image?
A. Similar
B. Less rich
C. More rich
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the color richness of the second image?\nA. Similar\nB. Less rich\nC. More rich\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.6562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5674,[Response]: A.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 816:  82%|▊| 817/1000 [08:54<
[Running Accuracy]: 0.5679,[Response]: C.<|endoftext|>, [Correct Ans]: More rich, , [Prog]: 817:  82%|▊| 817/1000 [08:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the color richness of the second image?\nA. Similar\nB. Less rich\nC. More rich\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how real is the second image?
A. More realistic
B. About the same
C. Less realistic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how real is the second image?
A. More realistic
B. About the same
C. Less realistic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how real is the second image?\nA. More realistic\nB. About the same\nC. Less realistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5679,[Response]: C.<|endoftext|>, [Correct Ans]: More rich, , [Prog]: 817:  82%|▊| 818/1000 [08:5
[Running Accuracy]: 0.5672,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 818:  82%|▊| 818/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how real is the second image?\nA. More realistic\nB. About the same\nC. Less realistic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image less sharp than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image less sharp than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image less sharp than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5672,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 818:  82%|▊| 819/1000 
[Running Accuracy]: 0.5678,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 819:  82%|▊| 819/1000 [08:55<02:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image less sharp than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most affected by motion blur?
A. car in the second image
B. sky in the first image
C. ground in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most affected by motion blur?
A. car in the second image
B. sky in the first image
C. ground in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most affected by motion blur?\nA. car in the second image\nB. sky in the first image\nC. ground in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5678,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 819:  82%|▊| 820/1000 [08:56<01:5
[Running Accuracy]: 0.5683,[Response]: A.<|endoftext|>, [Correct Ans]: car in the second image, , [Prog]: 820:  82%|▊| 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most affected by motion blur?\nA. car in the second image\nB. sky in the first image\nC. ground in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the composition of the first image better than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the composition of the first image better than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the composition of the first image better than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5683,[Response]: A.<|endoftext|>, [Correct Ans]: car in the second image, , [Prog]: 820:  82%|▊| 
[Running Accuracy]: 0.5688,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 821:  82%|▊| 821/1000 [08:57<02:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the composition of the first image better than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by motion blur?
A. The baby in the first image
B. The sky in the second image
C. The sea surface in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by motion blur?
A. The baby in the first image
B. The sky in the second image
C. The sea surface in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by motion blur?\nA. The baby in the first image\nB. The sky in the second image\nC. The sea surface in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5688,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 821:  82%|▊| 822/1000 [08:57<01:5
[Running Accuracy]: 0.5681,[Response]: C.<|endoftext|>, [Correct Ans]: The baby in the first image, , [Prog]: 822:  82%
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by motion blur?\nA. The baby in the first image\nB. The sky in the second image\nC. The sea surface in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very blurry?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very blurry?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very blurry?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5681,[Response]: C.<|endoftext|>, [Correct Ans]: The baby in the first image, , [Prog]: 822:  82%
[Running Accuracy]: 0.5687,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 823:  82%|▊| 823/1000 [08:58<01:51
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very blurry?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5687,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 823:  82%|▊| 824/1000 [08:58<01:45
[Running Accuracy]: 0.5680,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 824:  82%|▊| 824/1000 [08:58<01:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how real is the second image?
A. Less real
B. About the same
C. More real
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how real is the second image?
A. Less real
B. About the same
C. More real
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how real is the second image?\nA. Less real\nB. About the same\nC. More real\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5680,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 824:  82%|▊| 825/1000 [08:59<01:5
[Running Accuracy]: 0.5673,[Response]: A.<|endoftext|>, [Correct Ans]: More real, , [Prog]: 825:  82%|▊| 825/1000 [08:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how real is the second image?\nA. Less real\nB. About the same\nC. More real\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5673,[Response]: A.<|endoftext|>, [Correct Ans]: More real, , [Prog]: 825:  83%|▊| 826/1000 [09:0
[Running Accuracy]: 0.5666,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 826:  83%|▊| 826/1000 [09:00<01:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most affected by noise?
A. The sky in the first image
B. The bed in the second image
C. The person in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most affected by noise?
A. The sky in the first image
B. The bed in the second image
C. The person in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most affected by noise?\nA. The sky in the first image\nB. The bed in the second image\nC. The person in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5666,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 826:  83%|▊| 827/1000 [09:00<01:4
[Running Accuracy]: 0.5671,[Response]: B.<|endoftext|>, [Correct Ans]: The bed in the second image, , [Prog]: 827:  83%
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most affected by noise?\nA. The sky in the first image\nB. The bed in the second image\nC. The person in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more authentic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more authentic than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more authentic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5671,[Response]: B.<|endoftext|>, [Correct Ans]: The bed in the second image, , [Prog]: 827:  83%
[Running Accuracy]: 0.5664,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 828:  83%|▊| 828/1000 [09:01<01:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more authentic than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5664,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 828:  83%|▊| 829/1000 [09:01<01:4
[Running Accuracy]: 0.5657,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 829:  83%|▊| 829/1000 [09:01<01:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5657,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 829:  83%|▊| 830/1000 [09:02<01:4
[Running Accuracy]: 0.5651,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 830:  83%|▊| 830/1000 [09:02<01:47
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the color of the second image compare to the first image?
A. Similar
B. Richer
C. Less Rich
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the color of the second image compare to the first image?
A. Similar
B. Richer
C. Less Rich
Answer with the option's letter from the given choices directly.

prompts: [["How does the color of the second image compare to the first image?\nA. Similar\nB. Richer\nC. Less Rich\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.6875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5651,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 830:  83%|▊| 831/1000 [09:03<01:43
[Running Accuracy]: 0.5644,[Response]: C.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 831:  83%|▊| 831/1000 [09:03<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the color of the second image compare to the first image?\nA. Similar\nB. Richer\nC. Less Rich\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image more vivid than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image more vivid than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image more vivid than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5644,[Response]: C.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 831:  83%|▊| 832/1000 [09:03<0
[Running Accuracy]: 0.5637,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 832:  83%|▊| 832/1000 [09:03<01:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image more vivid than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5637,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 832:  83%|▊| 833/1000 [09:04<01:3
[Running Accuracy]: 0.5630,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 833:  83%|▊| 833/1000 [09:04<01:37
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very blurry?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very blurry?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very blurry?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5630,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 833:  83%|▊| 834/1000 [09:04<01:37
[Running Accuracy]: 0.5635,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 834:  83%|▊| 834/1000 [09:04<01:37
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very blurry?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. Background desktop of the first image
B. Pizza of the second image
C. Character of the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. Background desktop of the first image
B. Pizza of the second image
C. Character of the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. Background desktop of the first image\nB. Pizza of the second image\nC. Character of the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5635,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 834:  84%|▊| 835/1000 [09:05<01:34
[Running Accuracy]: 0.5629,[Response]: A.<|endoftext|>, [Correct Ans]: Pizza of the second image, , [Prog]: 835:  84%|▊
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. Background desktop of the first image\nB. Pizza of the second image\nC. Character of the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is there motion blur issue in these two images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is there motion blur issue in these two images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is there motion blur issue in these two images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5629,[Response]: A.<|endoftext|>, [Correct Ans]: Pizza of the second image, , [Prog]: 835:  84%|▊
[Running Accuracy]: 0.5634,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 836:  84%|▊| 836/1000 [09:06<01:34
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is there motion blur issue in these two images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5634,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 836:  84%|▊| 837/1000 [09:06<01:31
[Running Accuracy]: 0.5627,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 837:  84%|▊| 837/1000 [09:06<01:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5627,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 837:  84%|▊| 838/1000 [09:07<01:2
[Running Accuracy]: 0.5632,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 838:  84%|▊| 838/1000 [09:07<01:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is the least clear?
A. Background of the first image
B. Facial features of the first image
C. Facial features of the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is the least clear?
A. Background of the first image
B. Facial features of the first image
C. Facial features of the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is the least clear?\nA. Background of the first image\nB. Facial features of the first image\nC. Facial features of the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5632,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 838:  84%|▊| 839/1000 [09:07<01:3
[Running Accuracy]: 0.5638,[Response]: A.<|endoftext|>, [Correct Ans]: Background of the first image, , [Prog]: 839:  8
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is the least clear?\nA. Background of the first image\nB. Facial features of the first image\nC. Facial features of the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most severely affected by underexposure?
A. Background of the second image
B. Facial expression of the person in the second image
C. Pillow in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most severely affected by underexposure?
A. Background of the second image
B. Facial expression of the person in the second image
C. Pillow in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most severely affected by underexposure?\nA. Background of the second image\nB. Facial expression of the person in the second image\nC. Pillow in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5638,[Response]: A.<|endoftext|>, [Correct Ans]: Background of the first image, , [Prog]: 839:  8
[Running Accuracy]: 0.5631,[Response]: B.<|endoftext|>, [Correct Ans]: Pillow in the first image, , [Prog]: 840:  84%|▊
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most severely affected by underexposure?\nA. Background of the second image\nB. Facial expression of the person in the second image\nC. Pillow in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the texture detail in the second image?
A. Richer
B. About the same
C. Less rich
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the texture detail in the second image?
A. Richer
B. About the same
C. Less rich
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the texture detail in the second image?\nA. Richer\nB. About the same\nC. Less rich\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5631,[Response]: B.<|endoftext|>, [Correct Ans]: Pillow in the first image, , [Prog]: 840:  84%|▊
[Running Accuracy]: 0.5624,[Response]: A.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 841:  84%|▊| 841/1000 [09:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the texture detail in the second image?\nA. Richer\nB. About the same\nC. Less rich\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Clearer
B. About the same
C. Blurrier
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Clearer
B. About the same
C. Blurrier
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Clearer\nB. About the same\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5624,[Response]: A.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 841:  84%|▊| 842/1000 [09:0
[Running Accuracy]: 0.5618,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 842:  84%|▊| 842/1000 [09:09<
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Clearer\nB. About the same\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by motion blur?
A. Background of the second image
B. Person in the second image
C. Person in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by motion blur?
A. Background of the second image
B. Person in the second image
C. Person in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by motion blur?\nA. Background of the second image\nB. Person in the second image\nC. Person in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5618,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 842:  84%|▊| 843/1000 [09:10<
[Running Accuracy]: 0.5623,[Response]: C.<|endoftext|>, [Correct Ans]: Person in the first image, , [Prog]: 843:  84%|▊
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by motion blur?\nA. Background of the second image\nB. Person in the second image\nC. Person in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images relatively blurry?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images relatively blurry?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images relatively blurry?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5623,[Response]: C.<|endoftext|>, [Correct Ans]: Person in the first image, , [Prog]: 843:  84%|▊
[Running Accuracy]: 0.5628,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 844:  84%|▊| 844/1000 [09:10<01:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images relatively blurry?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the sharpness of the second image?
A. Similar
B. More blurry
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the sharpness of the second image?
A. Similar
B. More blurry
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the sharpness of the second image?\nA. Similar\nB. More blurry\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5628,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 844:  84%|▊| 845/1000 [09:11<01:2
[Running Accuracy]: 0.5633,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 845:  84%|▊| 845/1000 [09
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the sharpness of the second image?\nA. Similar\nB. More blurry\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5633,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 845:  85%|▊| 846/1000 [09
[Running Accuracy]: 0.5638,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 846:  85%|▊| 846/1000 [09:11<01:27
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5638,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 846:  85%|▊| 847/1000 [09:12<01:28
[Running Accuracy]: 0.5632,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 847:  85%|▊| 847/1000 [09:12<01:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5632,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 847:  85%|▊| 848/1000 [09:12<01:2
[Running Accuracy]: 0.5637,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 848:  85%|▊| 848/1000 [09:12<01:27
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Does the first image retain more texture details than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Does the first image retain more texture details than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Does the first image retain more texture details than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5637,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 848:  85%|▊| 849/1000 [09:13<01:25
[Running Accuracy]: 0.5630,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 849:  85%|▊| 849/1000 [09:13<01:25
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Does the first image retain more texture details than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by snowflake-like distortion?
A. the road in the second image
B. the background of the first image
C. the ground of the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by snowflake-like distortion?
A. the road in the second image
B. the background of the first image
C. the ground of the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by snowflake-like distortion?\nA. the road in the second image\nB. the background of the first image\nC. the ground of the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5630,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 849:  85%|▊| 850/1000 [09:13<01:23
[Running Accuracy]: 0.5635,[Response]: A.<|endoftext|>, [Correct Ans]: the road in the second image, , [Prog]: 850:  85
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by snowflake-like distortion?\nA. the road in the second image\nB. the background of the first image\nC. the ground of the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images relatively blurry?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images relatively blurry?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images relatively blurry?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5635,[Response]: A.<|endoftext|>, [Correct Ans]: the road in the second image, , [Prog]: 850:  85
[Running Accuracy]: 0.5640,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 851:  85%|▊| 851/1000 [09:14<01:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images relatively blurry?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5640,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 851:  85%|▊| 852/1000 [09:15<01:2
[Running Accuracy]: 0.5646,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 852:  85%|▊| 852/1000 [09:15<01:21
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5646,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 852:  85%|▊| 853/1000 [09:15<01:19
[Running Accuracy]: 0.5651,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 853:  85%|▊| 853/1000 [09:15<01:19
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image more vivid than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image more vivid than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image more vivid than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5651,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 853:  85%|▊| 854/1000 [09:16<01:21
[Running Accuracy]: 0.5656,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 854:  85%|▊| 854/1000 [09:16<01:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image more vivid than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by motion blur?
A. Road in the first image
B. Ground in the second image
C. Railing in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by motion blur?
A. Road in the first image
B. Ground in the second image
C. Railing in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by motion blur?\nA. Road in the first image\nB. Ground in the second image\nC. Railing in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5656,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 854:  86%|▊| 855/1000 [09:16<01:3
[Running Accuracy]: 0.5649,[Response]: A.<|endoftext|>, [Correct Ans]: Railing in the first image, , [Prog]: 855:  86%|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by motion blur?\nA. Road in the first image\nB. Ground in the second image\nC. Railing in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part has the richest texture details?
A. the dog in the first image
B. the background in the second image
C. the monster in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part has the richest texture details?
A. the dog in the first image
B. the background in the second image
C. the monster in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the richest texture details?\nA. the dog in the first image\nB. the background in the second image\nC. the monster in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5649,[Response]: A.<|endoftext|>, [Correct Ans]: Railing in the first image, , [Prog]: 855:  86%|
[Running Accuracy]: 0.5643,[Response]: A.<|endoftext|>, [Correct Ans]: the monster in the second image, , [Prog]: 856: 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part has the richest texture details?\nA. the dog in the first image\nB. the background in the second image\nC. the monster in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below suffers the most severe underexposure problem?
A. House windows in the second image
B. Banana in the first image
C. Facial features of the person in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below suffers the most severe underexposure problem?
A. House windows in the second image
B. Banana in the first image
C. Facial features of the person in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below suffers the most severe underexposure problem?\nA. House windows in the second image\nB. Banana in the first image\nC. Facial features of the person in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5643,[Response]: A.<|endoftext|>, [Correct Ans]: the monster in the second image, , [Prog]: 856: 
[Running Accuracy]: 0.5636,[Response]: B.<|endoftext|>, [Correct Ans]: House windows in the second image, , [Prog]: 857
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below suffers the most severe underexposure problem?\nA. House windows in the second image\nB. Banana in the first image\nC. Facial features of the person in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the colors of these two images both vivid?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the colors of these two images both vivid?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the colors of these two images both vivid?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5636,[Response]: B.<|endoftext|>, [Correct Ans]: House windows in the second image, , [Prog]: 857
[Running Accuracy]: 0.5641,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 858:  86%|▊| 858/1000 [09:19<01:34
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the colors of these two images both vivid?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the first image richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the first image richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5641,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 858:  86%|▊| 859/1000 [09:19<01:28
[Running Accuracy]: 0.5634,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 859:  86%|▊| 859/1000 [09:19<01:28
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the first image richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by motion blur?
A. Child in the first image
B. Sky in the second image
C. Tree on the left side in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by motion blur?
A. Child in the first image
B. Sky in the second image
C. Tree on the left side in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by motion blur?\nA. Child in the first image\nB. Sky in the second image\nC. Tree on the left side in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-29.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5634,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 859:  86%|▊| 860/1000 [09:20<01:34
[Running Accuracy]: 0.5628,[Response]: A.<|endoftext|>, [Correct Ans]: Tree on the left side in the second image, , [Pr
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by motion blur?\nA. Child in the first image\nB. Sky in the second image\nC. Tree on the left side in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most severely affected by motion blur?
A. The ball in the second image
B. The light source in the mirror in the first image
C. The person in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most severely affected by motion blur?
A. The ball in the second image
B. The light source in the mirror in the first image
C. The person in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most severely affected by motion blur?\nA. The ball in the second image\nB. The light source in the mirror in the first image\nC. The person in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5628,[Response]: A.<|endoftext|>, [Correct Ans]: Tree on the left side in the second image, , [Pr
[Running Accuracy]: 0.5621,[Response]: A.<|endoftext|>, [Correct Ans]: The light source in the mirror in the first imag
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most severely affected by motion blur?\nA. The ball in the second image\nB. The light source in the mirror in the first image\nC. The person in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most severely affected by the out-of-focus?
A. The background of the second image
B. The wall in the first image
C. The person in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most severely affected by the out-of-focus?
A. The background of the second image
B. The wall in the first image
C. The person in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most severely affected by the out-of-focus?\nA. The background of the second image\nB. The wall in the first image\nC. The person in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5000], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5621,[Response]: A.<|endoftext|>, [Correct Ans]: The light source in the mirror in the first imag
[Running Accuracy]: 0.5615,[Response]: C.<|endoftext|>, [Correct Ans]: The background of the second image, , [Prog]: 86
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most severely affected by the out-of-focus?\nA. The background of the second image\nB. The wall in the first image\nC. The person in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most severely affected by motion blur?
A. The figures in the second image
B. The figures in the first image
C. The trees in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most severely affected by motion blur?
A. The figures in the second image
B. The figures in the first image
C. The trees in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most severely affected by motion blur?\nA. The figures in the second image\nB. The figures in the first image\nC. The trees in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5615,[Response]: C.<|endoftext|>, [Correct Ans]: The background of the second image, , [Prog]: 86
[Running Accuracy]: 0.5608,[Response]: A.<|endoftext|>, [Correct Ans]: The trees in the second image, , [Prog]: 863:  8
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most severely affected by motion blur?\nA. The figures in the second image\nB. The figures in the first image\nC. The trees in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by motion blur?
A. The animal in the second image
B. The background in the second image
C. The person in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by motion blur?
A. The animal in the second image
B. The background in the second image
C. The person in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by motion blur?\nA. The animal in the second image\nB. The background in the second image\nC. The person in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5608,[Response]: A.<|endoftext|>, [Correct Ans]: The trees in the second image, , [Prog]: 863:  8
[Running Accuracy]: 0.5602,[Response]: A.<|endoftext|>, [Correct Ans]: The person in the first image, , [Prog]: 864:  8
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by motion blur?\nA. The animal in the second image\nB. The background in the second image\nC. The person in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5602,[Response]: A.<|endoftext|>, [Correct Ans]: The person in the first image, , [Prog]: 864:  8
[Running Accuracy]: 0.5595,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 865:  86%|▊| 865/1000 [09:23<01:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by motion blur?
A. The cue stick in the first image
B. The lower right merchandise in the second image
C. The person in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by motion blur?
A. The cue stick in the first image
B. The lower right merchandise in the second image
C. The person in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by motion blur?\nA. The cue stick in the first image\nB. The lower right merchandise in the second image\nC. The person in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5595,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 865:  87%|▊| 866/1000 [09:23<01:2
[Running Accuracy]: 0.5589,[Response]: B.<|endoftext|>, [Correct Ans]: The cue stick in the first image, , [Prog]: 866:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by motion blur?\nA. The cue stick in the first image\nB. The lower right merchandise in the second image\nC. The person in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5589,[Response]: B.<|endoftext|>, [Correct Ans]: The cue stick in the first image, , [Prog]: 866:
[Running Accuracy]: 0.5582,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 867:  87%|▊| 867/1000 [09:24<01:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the illumination in the second image?
A. Less Adequate
B. About the Same
C. More Adequate
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the illumination in the second image?
A. Less Adequate
B. About the Same
C. More Adequate
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the illumination in the second image?\nA. Less Adequate\nB. About the Same\nC. More Adequate\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5000], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5582,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 867:  87%|▊| 868/1000 [09:24<01:1
[Running Accuracy]: 0.5588,[Response]: C.<|endoftext|>, [Correct Ans]: More Adequate, , [Prog]: 868:  87%|▊| 868/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the illumination in the second image?\nA. Less Adequate\nB. About the Same\nC. More Adequate\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the color richness of the second image?
A. richer
B. similar
C. less rich
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the color richness of the second image?
A. richer
B. similar
C. less rich
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the color richness of the second image?\nA. richer\nB. similar\nC. less rich\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5588,[Response]: C.<|endoftext|>, [Correct Ans]: More Adequate, , [Prog]: 868:  87%|▊| 869/1000 [
[Running Accuracy]: 0.5581,[Response]: A.<|endoftext|>, [Correct Ans]: less rich, , [Prog]: 869:  87%|▊| 869/1000 [09:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the color richness of the second image?\nA. richer\nB. similar\nC. less rich\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5581,[Response]: A.<|endoftext|>, [Correct Ans]: less rich, , [Prog]: 869:  87%|▊| 870/1000 [09:2
[Running Accuracy]: 0.5575,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 870:  87%|▊| 870/1000 [09:26<01:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the texture detail of the first image richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the texture detail of the first image richer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the texture detail of the first image richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5575,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 870:  87%|▊| 871/1000 [09:26<01:1
[Running Accuracy]: 0.5580,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 871:  87%|▊| 871/1000 [09:26<01:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the texture detail of the first image richer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The front mountain of the first image
B. The sun in the upper right corner of the first image
C. Artwork in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The front mountain of the first image
B. The sun in the upper right corner of the first image
C. Artwork in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The front mountain of the first image\nB. The sun in the upper right corner of the first image\nC. Artwork in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.6562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5580,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 871:  87%|▊| 872/1000 [09:27<01:1
[Running Accuracy]: 0.5585,[Response]: B.<|endoftext|>, [Correct Ans]: The sun in the upper right corner of the first i
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The front mountain of the first image\nB. The sun in the upper right corner of the first image\nC. Artwork in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very blurry?
A. No
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very blurry?
A. No
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very blurry?\nA. No\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5585,[Response]: B.<|endoftext|>, [Correct Ans]: The sun in the upper right corner of the first i
[Running Accuracy]: 0.5578,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 873:  87%|▊| 873/1000 [09:27<01:14
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very blurry?\nA. No\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by motion blur?
A. Sunflower in the first image
B. Column in the second image
C. Shadow in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by motion blur?
A. Sunflower in the first image
B. Column in the second image
C. Shadow in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by motion blur?\nA. Sunflower in the first image\nB. Column in the second image\nC. Shadow in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5578,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 873:  87%|▊| 874/1000 [09:28<01:14
[Running Accuracy]: 0.5584,[Response]: C.<|endoftext|>, [Correct Ans]: Shadow in the second image, , [Prog]: 874:  87%|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by motion blur?\nA. Sunflower in the first image\nB. Column in the second image\nC. Shadow in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the composition of the second image?
A. Better
B. Worse
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the composition of the second image?
A. Better
B. Worse
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the composition of the second image?\nA. Better\nB. Worse\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5584,[Response]: C.<|endoftext|>, [Correct Ans]: Shadow in the second image, , [Prog]: 874:  88%|
[Running Accuracy]: 0.5577,[Response]: A.<|endoftext|>, [Correct Ans]: Worse, , [Prog]: 875:  88%|▉| 875/1000 [09:29<01
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the composition of the second image?\nA. Better\nB. Worse\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The figures in the first image
B. The light source of the house in the second image
C. The ground in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The figures in the first image
B. The light source of the house in the second image
C. The ground in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The figures in the first image\nB. The light source of the house in the second image\nC. The ground in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5577,[Response]: A.<|endoftext|>, [Correct Ans]: Worse, , [Prog]: 875:  88%|▉| 876/1000 [09:29<01
[Running Accuracy]: 0.5582,[Response]: B.<|endoftext|>, [Correct Ans]: The light source of the house in the second imag
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The figures in the first image\nB. The light source of the house in the second image\nC. The ground in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images overexposed?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images overexposed?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images overexposed?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5582,[Response]: B.<|endoftext|>, [Correct Ans]: The light source of the house in the second imag
[Running Accuracy]: 0.5587,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 877:  88%|▉| 877/1000 [09:30<01:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images overexposed?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5587,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 877:  88%|▉| 878/1000 [09:30<01:0
[Running Accuracy]: 0.5581,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 878:  88%|▉| 878/1000 [09:30<01:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5581,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 878:  88%|▉| 879/1000 [09:31<01:0
[Running Accuracy]: 0.5586,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 879:  88%|▉| 879/1000 [09:31<01:05
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most affected by motion blur?
A. The characters in the first image
B. The background of the first image
C. The faces of the characters in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most affected by motion blur?
A. The characters in the first image
B. The background of the first image
C. The faces of the characters in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most affected by motion blur?\nA. The characters in the first image\nB. The background of the first image\nC. The faces of the characters in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.6719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5586,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 879:  88%|▉| 880/1000 [09:31<01:04
[Running Accuracy]: 0.5580,[Response]: A.<|endoftext|>, [Correct Ans]: The background of the first image, , [Prog]: 880
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most affected by motion blur?\nA. The characters in the first image\nB. The background of the first image\nC. The faces of the characters in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by overexposure?
A. The moon in the second image
B. The person in the bottom right corner of the first image
C. The left sky in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by overexposure?
A. The moon in the second image
B. The person in the bottom right corner of the first image
C. The left sky in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by overexposure?\nA. The moon in the second image\nB. The person in the bottom right corner of the first image\nC. The left sky in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5580,[Response]: A.<|endoftext|>, [Correct Ans]: The background of the first image, , [Prog]: 880
[Running Accuracy]: 0.5585,[Response]: C.<|endoftext|>, [Correct Ans]: The left sky in the first image, , [Prog]: 881: 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by overexposure?\nA. The moon in the second image\nB. The person in the bottom right corner of the first image\nC. The left sky in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Similar
B. More blurry
C. Less clear
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Similar
B. More blurry
C. Less clear
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Similar\nB. More blurry\nC. Less clear\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5585,[Response]: C.<|endoftext|>, [Correct Ans]: The left sky in the first image, , [Prog]: 881: 
[Running Accuracy]: 0.5578,[Response]: C.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 882:  88%|▉| 882/1000 [09
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Similar\nB. More blurry\nC. Less clear\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, what is the richness of texture details in the second image?
A. Similar
B. Richer
C. Less rich
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, what is the richness of texture details in the second image?
A. Similar
B. Richer
C. Less rich
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, what is the richness of texture details in the second image?\nA. Similar\nB. Richer\nC. Less rich\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5578,[Response]: C.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 882:  88%|▉| 883/1000 [09
[Running Accuracy]: 0.5583,[Response]: B.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 883:  88%|▉| 883/1000 [09:33<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, what is the richness of texture details in the second image?\nA. Similar\nB. Richer\nC. Less rich\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the sharpness of the second image?
A. More blurry
B. Clearer
C. Similar
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the sharpness of the second image?
A. More blurry
B. Clearer
C. Similar
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the sharpness of the second image?\nA. More blurry\nB. Clearer\nC. Similar\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5583,[Response]: B.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 883:  88%|▉| 884/1000 [09:33<0
[Running Accuracy]: 0.5588,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 884:  88%|▉| 884/1000 [09
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the sharpness of the second image?\nA. More blurry\nB. Clearer\nC. Similar\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. About the same
B. Clearer
C. More blurry
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. About the same
B. Clearer
C. More blurry
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. About the same\nB. Clearer\nC. More blurry\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5588,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 884:  88%|▉| 885/1000 [09
[Running Accuracy]: 0.5593,[Response]: C.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 885:  88%|▉| 885/1000 [09
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. About the same\nB. Clearer\nC. More blurry\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the color richness of the second image?
A. Similar
B. Richer
C. More monotonous
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the color richness of the second image?
A. Similar
B. Richer
C. More monotonous
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the color richness of the second image?\nA. Similar\nB. Richer\nC. More monotonous\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5593,[Response]: C.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 885:  89%|▉| 886/1000 [09
[Running Accuracy]: 0.5598,[Response]: B.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 886:  89%|▉| 886/1000 [09:35<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the color richness of the second image?\nA. Similar\nB. Richer\nC. More monotonous\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5598,[Response]: B.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 886:  89%|▉| 887/1000 [09:35<0
[Running Accuracy]: 0.5592,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 887:  89%|▉| 887/1000 [09:35<01:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by motion blur?
A. The person riding a bike in the first image
B. The background of the first image
C. The plant in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by motion blur?
A. The person riding a bike in the first image
B. The background of the first image
C. The plant in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by motion blur?\nA. The person riding a bike in the first image\nB. The background of the first image\nC. The plant in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5592,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 887:  89%|▉| 888/1000 [09:36<01:0
[Running Accuracy]: 0.5597,[Response]: A.<|endoftext|>, [Correct Ans]: The person riding a bike in the first image, , [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by motion blur?\nA. The person riding a bike in the first image\nB. The background of the first image\nC. The plant in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The figures in the first image
B. The ground in the second image
C. The sun in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The figures in the first image
B. The ground in the second image
C. The sun in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The figures in the first image\nB. The ground in the second image\nC. The sun in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.6406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5597,[Response]: A.<|endoftext|>, [Correct Ans]: The person riding a bike in the first image, , [
[Running Accuracy]: 0.5602,[Response]: C.<|endoftext|>, [Correct Ans]: The sun in the first image, , [Prog]: 889:  89%|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The figures in the first image\nB. The ground in the second image\nC. The sun in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5602,[Response]: C.<|endoftext|>, [Correct Ans]: The sun in the first image, , [Prog]: 889:  89%|
[Running Accuracy]: 0.5596,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 890:  89%|▉| 890/1000 [09:37<01:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how does the authenticity of the second image compare?
A. Similar
B. Less realistic
C. More realistic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how does the authenticity of the second image compare?
A. Similar
B. Less realistic
C. More realistic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how does the authenticity of the second image compare?\nA. Similar\nB. Less realistic\nC. More realistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5596,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 890:  89%|▉| 891/1000 [09:38<01:0
[Running Accuracy]: 0.5600,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 891:  89%|▉| 891/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how does the authenticity of the second image compare?\nA. Similar\nB. Less realistic\nC. More realistic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how does the authenticity of the second image compare?
A. Similar
B. More authentic
C. Less authentic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how does the authenticity of the second image compare?
A. Similar
B. More authentic
C. Less authentic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how does the authenticity of the second image compare?\nA. Similar\nB. More authentic\nC. Less authentic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5600,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 891:  89%|▉| 892/1000 
[Running Accuracy]: 0.5594,[Response]: C.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 892:  89%|▉| 892/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how does the authenticity of the second image compare?\nA. Similar\nB. More authentic\nC. Less authentic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the clarity of the first image higher than that of the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the clarity of the first image higher than that of the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the clarity of the first image higher than that of the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5594,[Response]: C.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 892:  89%|▉| 893/1000 
[Running Accuracy]: 0.5599,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 893:  89%|▉| 893/1000 [09:39<01:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the clarity of the first image higher than that of the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5599,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 893:  89%|▉| 894/1000 [09:39<01:0
[Running Accuracy]: 0.5593,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 894:  89%|▉| 894/1000 [09:39<01:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The left side of the second image
B. The dog in the second image
C. The figures in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The left side of the second image
B. The dog in the second image
C. The figures in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The left side of the second image\nB. The dog in the second image\nC. The figures in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5593,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 894:  90%|▉| 895/1000 [09:40<00:5
[Running Accuracy]: 0.5598,[Response]: A.<|endoftext|>, [Correct Ans]: The left side of the second image, , [Prog]: 895
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The left side of the second image\nB. The dog in the second image\nC. The figures in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the two images both relatively clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the two images both relatively clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the two images both relatively clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5598,[Response]: A.<|endoftext|>, [Correct Ans]: The left side of the second image, , [Prog]: 895
[Running Accuracy]: 0.5603,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 896:  90%|▉| 896/1000 [09:40<00:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the two images both relatively clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5603,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 896:  90%|▉| 897/1000 [09:41<00:5
[Running Accuracy]: 0.5608,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 897:  90%|▉| 897/1000 [09:41<00:57
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more realistic than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5608,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 897:  90%|▉| 898/1000 [09:42<00:57
[Running Accuracy]: 0.5612,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 898:  90%|▉| 898/1000 [09:42<00:57
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more realistic than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images relatively clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images relatively clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images relatively clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-29.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5612,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 898:  90%|▉| 899/1000 [09:42<00:58
[Running Accuracy]: 0.5606,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 899:  90%|▉| 899/1000 [09:42<00:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images relatively clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The light source in the first image
B. The background in the second image
C. The apple in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The light source in the first image
B. The background in the second image
C. The apple in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The light source in the first image\nB. The background in the second image\nC. The apple in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5606,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 899:  90%|▉| 900/1000 [09:43<00:5
[Running Accuracy]: 0.5611,[Response]: A.<|endoftext|>, [Correct Ans]: The light source in the first image, , [Prog]: 9
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The light source in the first image\nB. The background in the second image\nC. The apple in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, is the second image more realistic?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, is the second image more realistic?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, is the second image more realistic?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5611,[Response]: A.<|endoftext|>, [Correct Ans]: The light source in the first image, , [Prog]: 9
[Running Accuracy]: 0.5605,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 901:  90%|▉| 901/1000 [09:43<00:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, is the second image more realistic?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images not very clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images not very clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images not very clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5605,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 901:  90%|▉| 902/1000 [09:44<00:5
[Running Accuracy]: 0.5610,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 902:  90%|▉| 902/1000 [09:44<00:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images not very clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how does the authenticity of the second image compare?
A. More authentic
B. Less authentic
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how does the authenticity of the second image compare?
A. More authentic
B. Less authentic
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how does the authenticity of the second image compare?\nA. More authentic\nB. Less authentic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5610,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 902:  90%|▉| 903/1000 [09:45<00:5
[Running Accuracy]: 0.5604,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 903:  90%|▉| 903/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how does the authenticity of the second image compare?\nA. More authentic\nB. Less authentic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by motion blur?
A. Background of the first image
B. Background of the second image
C. Person in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by motion blur?
A. Background of the first image
B. Background of the second image
C. Person in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by motion blur?\nA. Background of the first image\nB. Background of the second image\nC. Person in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5604,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 903:  90%|▉| 904/1000 
[Running Accuracy]: 0.5597,[Response]: C.<|endoftext|>, [Correct Ans]: Background of the second image, , [Prog]: 904:  
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by motion blur?\nA. Background of the first image\nB. Background of the second image\nC. Person in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how true is the second image?
A. More realistic
B. About the same
C. Less realistic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how true is the second image?
A. More realistic
B. About the same
C. Less realistic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how true is the second image?\nA. More realistic\nB. About the same\nC. Less realistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5597,[Response]: C.<|endoftext|>, [Correct Ans]: Background of the second image, , [Prog]: 904:  
[Running Accuracy]: 0.5591,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 905:  90%|▉| 905/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how true is the second image?\nA. More realistic\nB. About the same\nC. Less realistic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the realism of the second image compare to the first image?
A. Similar
B. More realistic
C. Less realistic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the realism of the second image compare to the first image?
A. Similar
B. More realistic
C. Less realistic
Answer with the option's letter from the given choices directly.

prompts: [["How does the realism of the second image compare to the first image?\nA. Similar\nB. More realistic\nC. Less realistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5591,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 905:  91%|▉| 906/1000 
[Running Accuracy]: 0.5585,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 906:  91%|▉| 906/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the realism of the second image compare to the first image?\nA. Similar\nB. More realistic\nC. Less realistic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how's the authenticity of the second image?
A. Similar
B. Less authentic
C. More authentic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how's the authenticity of the second image?
A. Similar
B. Less authentic
C. More authentic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how's the authenticity of the second image?\nA. Similar\nB. Less authentic\nC. More authentic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5585,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 906:  91%|▉| 907/1000 
[Running Accuracy]: 0.5579,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 907:  91%|▉| 907/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how's the authenticity of the second image?\nA. Similar\nB. Less authentic\nC. More authentic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by motion blur?
A. The ground in the second image
B. The cyclist in the second image
C. The person in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by motion blur?
A. The ground in the second image
B. The cyclist in the second image
C. The person in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by motion blur?\nA. The ground in the second image\nB. The cyclist in the second image\nC. The person in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5579,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 907:  91%|▉| 908/1000 
[Running Accuracy]: 0.5584,[Response]: B.<|endoftext|>, [Correct Ans]: The cyclist in the second image, , [Prog]: 908: 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by motion blur?\nA. The ground in the second image\nB. The cyclist in the second image\nC. The person in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the authenticity of the second image?
A. More authentic
B. About the same
C. Less authentic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the authenticity of the second image?
A. More authentic
B. About the same
C. Less authentic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the authenticity of the second image?\nA. More authentic\nB. About the same\nC. Less authentic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5584,[Response]: B.<|endoftext|>, [Correct Ans]: The cyclist in the second image, , [Prog]: 908: 
[Running Accuracy]: 0.5578,[Response]: C.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 909:  91%|▉| 909/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the authenticity of the second image?\nA. More authentic\nB. About the same\nC. Less authentic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how rich are the texture details in the second image?
A. Similar
B. Less rich
C. More rich
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how rich are the texture details in the second image?
A. Similar
B. Less rich
C. More rich
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how rich are the texture details in the second image?\nA. Similar\nB. Less rich\nC. More rich\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5578,[Response]: C.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 909:  91%|▉| 910/1000 
[Running Accuracy]: 0.5582,[Response]: B.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 910:  91%|▉| 910/1000 [09:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how rich are the texture details in the second image?\nA. Similar\nB. Less rich\nC. More rich\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how does the authenticity of the second image compare?
A. Similar
B. Less realistic
C. More realistic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how does the authenticity of the second image compare?
A. Similar
B. Less realistic
C. More realistic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how does the authenticity of the second image compare?\nA. Similar\nB. Less realistic\nC. More realistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5582,[Response]: B.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 910:  91%|▉| 911/1000 [09:5
[Running Accuracy]: 0.5576,[Response]: B.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 911:  91%|▉| 911/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how does the authenticity of the second image compare?\nA. Similar\nB. Less realistic\nC. More realistic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How is the fidelity of the second image compared to the first image?
A. More realistic
B. Less realistic
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How is the fidelity of the second image compared to the first image?
A. More realistic
B. Less realistic
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["How is the fidelity of the second image compared to the first image?\nA. More realistic\nB. Less realistic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5576,[Response]: B.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 911:  91%|▉| 912/1000 
[Running Accuracy]: 0.5570,[Response]: B.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 912:  91%|▉| 912/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How is the fidelity of the second image compared to the first image?\nA. More realistic\nB. Less realistic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most affected by noise?
A. Legs of the character in the second image
B. Sky in the first image
C. Ground in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most affected by noise?
A. Legs of the character in the second image
B. Sky in the first image
C. Ground in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most affected by noise?\nA. Legs of the character in the second image\nB. Sky in the first image\nC. Ground in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5570,[Response]: B.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 912:  91%|▉| 913/1000 
[Running Accuracy]: 0.5564,[Response]: C.<|endoftext|>, [Correct Ans]: Legs of the character in the second image, , [Pr
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most affected by noise?\nA. Legs of the character in the second image\nB. Sky in the first image\nC. Ground in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how does the realism of the second image compare?
A. More realistic
B. Less realistic
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how does the realism of the second image compare?
A. More realistic
B. Less realistic
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how does the realism of the second image compare?\nA. More realistic\nB. Less realistic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5564,[Response]: C.<|endoftext|>, [Correct Ans]: Legs of the character in the second image, , [Pr
[Running Accuracy]: 0.5558,[Response]: B.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 914:  91%|▉| 914/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how does the realism of the second image compare?\nA. More realistic\nB. Less realistic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the sharpness of the first image higher than that of the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the sharpness of the first image higher than that of the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the sharpness of the first image higher than that of the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5558,[Response]: B.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 914:  92%|▉| 915/1000 
[Running Accuracy]: 0.5563,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 915:  92%|▉| 915/1000 [09:52<00:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the sharpness of the first image higher than that of the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how does the reality of the second image compare?
A. Less real
B. About the same
C. More real
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how does the reality of the second image compare?
A. Less real
B. About the same
C. More real
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how does the reality of the second image compare?\nA. Less real\nB. About the same\nC. More real\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5563,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 915:  92%|▉| 916/1000 [09:53<00:5
[Running Accuracy]: 0.5557,[Response]: B.<|endoftext|>, [Correct Ans]: More real, , [Prog]: 916:  92%|▉| 916/1000 [09:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how does the reality of the second image compare?\nA. Less real\nB. About the same\nC. More real\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5557,[Response]: B.<|endoftext|>, [Correct Ans]: More real, , [Prog]: 916:  92%|▉| 917/1000 [09:5
[Running Accuracy]: 0.5551,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 917:  92%|▉| 917/1000 [09:54<00:55
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images very clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images very clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5551,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 917:  92%|▉| 918/1000 [09:54<00:51
[Running Accuracy]: 0.5556,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 918:  92%|▉| 918/1000 [09:54<00:51
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images very clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how would you describe the authenticity of the second image?
A. about the same
B. more authentic
C. less authentic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how would you describe the authenticity of the second image?
A. about the same
B. more authentic
C. less authentic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how would you describe the authenticity of the second image?\nA. about the same\nB. more authentic\nC. less authentic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5556,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 918:  92%|▉| 919/1000 [09:55<00:51
[Running Accuracy]: 0.5550,[Response]: C.<|endoftext|>, [Correct Ans]: about the same, , [Prog]: 919:  92%|▉| 919/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how would you describe the authenticity of the second image?\nA. about the same\nB. more authentic\nC. less authentic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the sharpness of the first image higher than that of the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the sharpness of the first image higher than that of the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the sharpness of the first image higher than that of the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5550,[Response]: C.<|endoftext|>, [Correct Ans]: about the same, , [Prog]: 919:  92%|▉| 920/1000 
[Running Accuracy]: 0.5543,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 920:  92%|▉| 920/1000 [09:56<00:5
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the sharpness of the first image higher than that of the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part has the richest texture detail?
A. The background of the second image
B. The lion in the second image
C. The background of the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part has the richest texture detail?
A. The background of the second image
B. The lion in the second image
C. The background of the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the richest texture detail?\nA. The background of the second image\nB. The lion in the second image\nC. The background of the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5543,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 920:  92%|▉| 921/1000 [09:56<00:5
[Running Accuracy]: 0.5548,[Response]: B.<|endoftext|>, [Correct Ans]: The lion in the second image, , [Prog]: 921:  92
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part has the richest texture detail?\nA. The background of the second image\nB. The lion in the second image\nC. The background of the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how does the authenticity of the second image fare?
A. More authentic
B. Less authentic
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how does the authenticity of the second image fare?
A. More authentic
B. Less authentic
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how does the authenticity of the second image fare?\nA. More authentic\nB. Less authentic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5548,[Response]: B.<|endoftext|>, [Correct Ans]: The lion in the second image, , [Prog]: 921:  92
[Running Accuracy]: 0.5542,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 922:  92%|▉| 922/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how does the authenticity of the second image fare?\nA. More authentic\nB. Less authentic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by motion blur?
A. The wall in the first image
B. The character in the first image
C. The building in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by motion blur?
A. The wall in the first image
B. The character in the first image
C. The building in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by motion blur?\nA. The wall in the first image\nB. The character in the first image\nC. The building in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.5156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5542,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 922:  92%|▉| 923/1000 
[Running Accuracy]: 0.5547,[Response]: B.<|endoftext|>, [Correct Ans]: The character in the first image, , [Prog]: 923:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by motion blur?\nA. The wall in the first image\nB. The character in the first image\nC. The building in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is least affected by motion blur?
A. Cyclist in the first image
B. Background vehicle in the first image
C. Ground in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is least affected by motion blur?
A. Cyclist in the first image
B. Background vehicle in the first image
C. Ground in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is least affected by motion blur?\nA. Cyclist in the first image\nB. Background vehicle in the first image\nC. Ground in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5547,[Response]: B.<|endoftext|>, [Correct Ans]: The character in the first image, , [Prog]: 923:
[Running Accuracy]: 0.5541,[Response]: C.<|endoftext|>, [Correct Ans]: Cyclist in the first image, , [Prog]: 924:  92%|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is least affected by motion blur?\nA. Cyclist in the first image\nB. Background vehicle in the first image\nC. Ground in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most seriously affected by motion blur?
A. The vehicles in the second image
B. The background in the first image
C. The people in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most seriously affected by motion blur?
A. The vehicles in the second image
B. The background in the first image
C. The people in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most seriously affected by motion blur?\nA. The vehicles in the second image\nB. The background in the first image\nC. The people in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5541,[Response]: C.<|endoftext|>, [Correct Ans]: Cyclist in the first image, , [Prog]: 924:  92%|
[Running Accuracy]: 0.5535,[Response]: A.<|endoftext|>, [Correct Ans]: The background in the first image, , [Prog]: 925
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most seriously affected by motion blur?\nA. The vehicles in the second image\nB. The background in the first image\nC. The people in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part has the richest texture details?
A. The animal in the second image
B. The person in the first image
C. The background of the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part has the richest texture details?
A. The animal in the second image
B. The person in the first image
C. The background of the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the richest texture details?\nA. The animal in the second image\nB. The person in the first image\nC. The background of the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5535,[Response]: A.<|endoftext|>, [Correct Ans]: The background in the first image, , [Prog]: 925
[Running Accuracy]: 0.5540,[Response]: A.<|endoftext|>, [Correct Ans]: The animal in the second image, , [Prog]: 926:  
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part has the richest texture details?\nA. The animal in the second image\nB. The person in the first image\nC. The background of the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The sun in the second image
B. The trees in the second image
C. The person in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The sun in the second image
B. The trees in the second image
C. The person in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The sun in the second image\nB. The trees in the second image\nC. The person in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5540,[Response]: A.<|endoftext|>, [Correct Ans]: The animal in the second image, , [Prog]: 926:  
[Running Accuracy]: 0.5545,[Response]: A.<|endoftext|>, [Correct Ans]: The sun in the second image, , [Prog]: 927:  93%
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The sun in the second image\nB. The trees in the second image\nC. The person in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how does the authenticity of the second image change?
A. Less authentic
B. More authentic
C. Similar
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how does the authenticity of the second image change?
A. Less authentic
B. More authentic
C. Similar
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how does the authenticity of the second image change?\nA. Less authentic\nB. More authentic\nC. Similar\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5545,[Response]: A.<|endoftext|>, [Correct Ans]: The sun in the second image, , [Prog]: 927:  93%
[Running Accuracy]: 0.5539,[Response]: A.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 928:  93%|▉| 928/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how does the authenticity of the second image change?\nA. Less authentic\nB. More authentic\nC. Similar\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5539,[Response]: A.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 928:  93%|▉| 929/1000 
[Running Accuracy]: 0.5533,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 929:  93%|▉| 929/1000 [10:01<00:4
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how does the authenticity of the second image compare?
A. More authentic
B. About the same
C. Less authentic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how does the authenticity of the second image compare?
A. More authentic
B. About the same
C. Less authentic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how does the authenticity of the second image compare?\nA. More authentic\nB. About the same\nC. Less authentic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5533,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 929:  93%|▉| 930/1000 [10:02<00:4
[Running Accuracy]: 0.5527,[Response]: C.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 930:  93%|▉| 930/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how does the authenticity of the second image compare?\nA. More authentic\nB. About the same\nC. Less authentic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5527,[Response]: C.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 930:  93%|▉| 931/1000 
[Running Accuracy]: 0.5521,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 931:  93%|▉| 931/1000 [10:02<00:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both of these images relatively clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both of these images relatively clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images relatively clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5521,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 931:  93%|▉| 932/1000 [10:03<00:3
[Running Accuracy]: 0.5526,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 932:  93%|▉| 932/1000 [10:03<00:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both of these images relatively clear?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5526,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 932:  93%|▉| 933/1000 [10:03<00:3
[Running Accuracy]: 0.5531,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 933:  93%|▉| 933/1000 [10:03<00:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part has the richest texture details?
A. The hair of the person in the first image
B. The background of the first image
C. The background of the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part has the richest texture details?
A. The hair of the person in the first image
B. The background of the first image
C. The background of the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the richest texture details?\nA. The hair of the person in the first image\nB. The background of the first image\nC. The background of the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5531,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 933:  93%|▉| 934/1000 [10:04<00:3
[Running Accuracy]: 0.5535,[Response]: A.<|endoftext|>, [Correct Ans]: The hair of the person in the first image, , [Pr
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part has the richest texture details?\nA. The hair of the person in the first image\nB. The background of the first image\nC. The background of the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. Sky in the first image
B. Sky in the second image
C. Person in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. Sky in the first image
B. Sky in the second image
C. Person in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. Sky in the first image\nB. Sky in the second image\nC. Person in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5535,[Response]: A.<|endoftext|>, [Correct Ans]: The hair of the person in the first image, , [Pr
[Running Accuracy]: 0.5529,[Response]: A.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 935:  94%|▉| 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. Sky in the first image\nB. Sky in the second image\nC. Person in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the texture detail of the first image much richer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the texture detail of the first image much richer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the texture detail of the first image much richer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-29.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5529,[Response]: A.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 935:  94%|▉| 
[Running Accuracy]: 0.5524,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 936:  94%|▉| 936/1000 [10:06<00:45
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the texture detail of the first image much richer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which of the following images does not have any distortion issues?
A. overexposure
B. lens flare
C. noise
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which of the following images does not have any distortion issues?
A. overexposure
B. lens flare
C. noise
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following images does not have any distortion issues?\nA. overexposure\nB. lens flare\nC. noise\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5524,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 936:  94%|▉| 937/1000 [10:06<00:46
[Running Accuracy]: 0.5528,[Response]: C.<|endoftext|>, [Correct Ans]: noise, , [Prog]: 937:  94%|▉| 937/1000 [10:06<00
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which of the following images does not have any distortion issues?\nA. overexposure\nB. lens flare\nC. noise\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Less clear
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Less clear
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Less clear\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5528,[Response]: C.<|endoftext|>, [Correct Ans]: noise, , [Prog]: 937:  94%|▉| 938/1000 [10:07<00
[Running Accuracy]: 0.5522,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 938:  94%|▉| 938/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Less clear\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part has the richest texture details?
A. Background of the first image
B. Fox in the first image
C. Frog in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part has the richest texture details?
A. Background of the first image
B. Fox in the first image
C. Frog in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the richest texture details?\nA. Background of the first image\nB. Fox in the first image\nC. Frog in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5522,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 938:  94%|▉| 939/1000 
[Running Accuracy]: 0.5517,[Response]: B.<|endoftext|>, [Correct Ans]: Frog in the second image, , [Prog]: 939:  94%|▉|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part has the richest texture details?\nA. Background of the first image\nB. Fox in the first image\nC. Frog in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by defocusing?
A. The flower in the second image
B. The lips in the first image
C. The ground in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by defocusing?
A. The flower in the second image
B. The lips in the first image
C. The ground in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by defocusing?\nA. The flower in the second image\nB. The lips in the first image\nC. The ground in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5517,[Response]: B.<|endoftext|>, [Correct Ans]: Frog in the second image, , [Prog]: 939:  94%|▉|
[Running Accuracy]: 0.5521,[Response]: A.<|endoftext|>, [Correct Ans]: The flower in the second image, , [Prog]: 940:  
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by defocusing?\nA. The flower in the second image\nB. The lips in the first image\nC. The ground in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by defocusing?
A. The quilt in the first image
B. The background of the second image
C. The background of the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by defocusing?
A. The quilt in the first image
B. The background of the second image
C. The background of the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by defocusing?\nA. The quilt in the first image\nB. The background of the second image\nC. The background of the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5521,[Response]: A.<|endoftext|>, [Correct Ans]: The flower in the second image, , [Prog]: 940:  
[Running Accuracy]: 0.5526,[Response]: B.<|endoftext|>, [Correct Ans]: The background of the second image, , [Prog]: 94
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by defocusing?\nA. The quilt in the first image\nB. The background of the second image\nC. The background of the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>How does the texture detail of the second image compare to the first image?
A. Similar
B. Less rich
C. Richer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the texture detail of the second image compare to the first image?
A. Similar
B. Less rich
C. Richer
Answer with the option's letter from the given choices directly.

prompts: [["How does the texture detail of the second image compare to the first image?\nA. Similar\nB. Less rich\nC. Richer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5526,[Response]: B.<|endoftext|>, [Correct Ans]: The background of the second image, , [Prog]: 94
[Running Accuracy]: 0.5520,[Response]: B.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 942:  94%|▉| 942/1000 [10:09<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>How does the texture detail of the second image compare to the first image?\nA. Similar\nB. Less rich\nC. Richer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by defocusing?
A. Background of the first image
B. Person in the second image
C. Background of the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by defocusing?
A. Background of the first image
B. Person in the second image
C. Background of the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by defocusing?\nA. Background of the first image\nB. Person in the second image\nC. Background of the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5520,[Response]: B.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 942:  94%|▉| 943/1000 [10:10<0
[Running Accuracy]: 0.5514,[Response]: B.<|endoftext|>, [Correct Ans]: Background of the first image, , [Prog]: 943:  9
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by defocusing?\nA. Background of the first image\nB. Person in the second image\nC. Background of the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5514,[Response]: B.<|endoftext|>, [Correct Ans]: Background of the first image, , [Prog]: 943:  9
[Running Accuracy]: 0.5519,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 944:  94%|▉| 944/1000 [10:10<00:32
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5519,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 944:  94%|▉| 945/1000 [10:11<00:31
[Running Accuracy]: 0.5513,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 945:  94%|▉| 945/1000 [10:11<00:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are there any distortion issues in these two images?
A. overexposure
B. motion blur
C. out of focus
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are there any distortion issues in these two images?
A. overexposure
B. motion blur
C. out of focus
Answer with the option's letter from the given choices directly.

prompts: [["Are there any distortion issues in these two images?\nA. overexposure\nB. motion blur\nC. out of focus\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.5938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5513,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 945:  95%|▉| 946/1000 [10:11<00:3
[Running Accuracy]: 0.5507,[Response]: B.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 946:  95%|▉| 946/1000 [1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are there any distortion issues in these two images?\nA. overexposure\nB. motion blur\nC. out of focus\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how real is the second image?
A. More real
B. Less real
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how real is the second image?
A. More real
B. Less real
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how real is the second image?\nA. More real\nB. Less real\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5507,[Response]: B.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 946:  95%|▉| 947/1000 [1
[Running Accuracy]: 0.5512,[Response]: A.<|endoftext|>, [Correct Ans]: More real, , [Prog]: 947:  95%|▉| 947/1000 [10:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how real is the second image?\nA. More real\nB. Less real\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5512,[Response]: A.<|endoftext|>, [Correct Ans]: More real, , [Prog]: 947:  95%|▉| 948/1000 [10:1
[Running Accuracy]: 0.5506,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 948:  95%|▉| 948/1000 [10:13<00:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image clearer than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5506,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 948:  95%|▉| 949/1000 [10:13<00:2
[Running Accuracy]: 0.5511,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 949:  95%|▉| 949/1000 [10:13<00:29
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image clearer than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the sharpness of the second image?
A. More severe
B. About the same
C. Slightly less
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the sharpness of the second image?
A. More severe
B. About the same
C. Slightly less
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the sharpness of the second image?\nA. More severe\nB. About the same\nC. Slightly less\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5511,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 949:  95%|▉| 950/1000 [10:14<00:27
[Running Accuracy]: 0.5505,[Response]: B.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 950:  95%|▉| 950/1000 [10
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the sharpness of the second image?\nA. More severe\nB. About the same\nC. Slightly less\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how severe is the impact of motion blur on the second image?
A. About the same
B. Less severe
C. More severe
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how severe is the impact of motion blur on the second image?
A. About the same
B. Less severe
C. More severe
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how severe is the impact of motion blur on the second image?\nA. About the same\nB. Less severe\nC. More severe\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5505,[Response]: B.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 950:  95%|▉| 951/1000 [10
[Running Accuracy]: 0.5510,[Response]: C.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 951:  95%|▉| 951/1000 [10
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how severe is the impact of motion blur on the second image?\nA. About the same\nB. Less severe\nC. More severe\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how severely is the second image affected by motion blur?
A. Less severe
B. More severe
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how severely is the second image affected by motion blur?
A. Less severe
B. More severe
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how severely is the second image affected by motion blur?\nA. Less severe\nB. More severe\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5510,[Response]: C.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 951:  95%|▉| 952/1000 [10
[Running Accuracy]: 0.5515,[Response]: B.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 952:  95%|▉| 952/1000 [10
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how severely is the second image affected by motion blur?\nA. Less severe\nB. More severe\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image more aesthetically pleasing than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image more aesthetically pleasing than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more aesthetically pleasing than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5515,[Response]: B.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 952:  95%|▉| 953/1000 [10
[Running Accuracy]: 0.5509,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 953:  95%|▉| 953/1000 [10:16<00:34
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image more aesthetically pleasing than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the noise in the first image larger than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the noise in the first image larger than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the noise in the first image larger than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5000], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5509,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 953:  95%|▉| 954/1000 [10:17<00:36
[Running Accuracy]: 0.5503,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 954:  95%|▉| 954/1000 [10:17<00:36
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the noise in the first image larger than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how blurry is the first image?
A. More blurry
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how blurry is the first image?
A. More blurry
B. About the same
C. Clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how blurry is the first image?\nA. More blurry\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5503,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 954:  96%|▉| 955/1000 [10:18<00:37
[Running Accuracy]: 0.5508,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 955:  96%|▉| 955/1000 [10
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how blurry is the first image?\nA. More blurry\nB. About the same\nC. Clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part of these two images is relatively clearer?
A. Trees in the first image
B. Wooden ground in the first image
C. Lamp in the second image
D. Sky in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part of these two images is relatively clearer?
A. Trees in the first image
B. Wooden ground in the first image
C. Lamp in the second image
D. Sky in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part of these two images is relatively clearer?\nA. Trees in the first image\nB. Wooden ground in the first image\nC. Lamp in the second image\nD. Sky in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5508,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 955:  96%|▉| 956/1000 [10
[Running Accuracy]: 0.5513,[Response]: C.<|endoftext|>, [Correct Ans]: Lamp in the second image, , [Prog]: 956:  96%|▉|
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part of these two images is relatively clearer?\nA. Trees in the first image\nB. Wooden ground in the first image\nC. Lamp in the second image\nD. Sky in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below shows the most obvious overexposure?
A. The book in the second chapter image
B. The woman in the second chapter image
C. The wall below in the first image
D. The text above in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below shows the most obvious overexposure?
A. The book in the second chapter image
B. The woman in the second chapter image
C. The wall below in the first image
D. The text above in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below shows the most obvious overexposure?\nA. The book in the second chapter image\nB. The woman in the second chapter image\nC. The wall below in the first image\nD. The text above in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5513,[Response]: C.<|endoftext|>, [Correct Ans]: Lamp in the second image, , [Prog]: 956:  96%|▉|
[Running Accuracy]: 0.5517,[Response]: C.<|endoftext|>, [Correct Ans]: The wall below in the first image, , [Prog]: 957
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below shows the most obvious overexposure?\nA. The book in the second chapter image\nB. The woman in the second chapter image\nC. The wall below in the first image\nD. The text above in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>In these two images, what kind of distortion did not appear?
A. Underexposure
B. Low light
C. Overexposure
D. Blur
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:In these two images, what kind of distortion did not appear?
A. Underexposure
B. Low light
C. Overexposure
D. Blur
Answer with the option's letter from the given choices directly.

prompts: [["In these two images, what kind of distortion did not appear?\nA. Underexposure\nB. Low light\nC. Overexposure\nD. Blur\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5517,[Response]: C.<|endoftext|>, [Correct Ans]: The wall below in the first image, , [Prog]: 957
[Running Accuracy]: 0.5511,[Response]: A.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 958:  96%|▉| 958/1000 [1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>In these two images, what kind of distortion did not appear?\nA. Underexposure\nB. Low light\nC. Overexposure\nD. Blur\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the composition aesthetics of the first image better than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the composition aesthetics of the first image better than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the composition aesthetics of the first image better than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5511,[Response]: A.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 958:  96%|▉| 959/1000 [1
[Running Accuracy]: 0.5506,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 959:  96%|▉| 959/1000 [10:21<00:33
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the composition aesthetics of the first image better than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image blurrier than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image blurrier than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image blurrier than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5506,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 959:  96%|▉| 960/1000 [10:22<00:33
[Running Accuracy]: 0.5510,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 960:  96%|▉| 960/1000 [10:22<00:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image blurrier than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the lighting in the second image?
A. Very dark
B. Much darker
C. Much brighter
D. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the lighting in the second image?
A. Very dark
B. Much darker
C. Much brighter
D. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the lighting in the second image?\nA. Very dark\nB. Much darker\nC. Much brighter\nD. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.5510,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 960:  96%|▉| 961/1000 [10:22<00:2
[Running Accuracy]: 0.5505,[Response]: D.<|endoftext|>, [Correct Ans]: Much brighter, , [Prog]: 961:  96%|▉| 961/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the lighting in the second image?\nA. Very dark\nB. Much darker\nC. Much brighter\nD. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image has higher clarity?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image has higher clarity?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.

prompts: [["Which image has higher clarity?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1250], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5505,[Response]: D.<|endoftext|>, [Correct Ans]: Much brighter, , [Prog]: 961:  96%|▉| 962/1000 [
[Running Accuracy]: 0.5509,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 962:  96%|▉| 962/1000 [1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image has higher clarity?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Much lower
B. Much higher
C. Much much higher
D. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Much lower
B. Much higher
C. Much much higher
D. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Much lower\nB. Much higher\nC. Much much higher\nD. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5509,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 962:  96%|▉| 963/1000 [1
[Running Accuracy]: 0.5514,[Response]: A.<|endoftext|>, [Correct Ans]: Much lower, , [Prog]: 963:  96%|▉| 963/1000 [10:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Much lower\nB. Much higher\nC. Much much higher\nD. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Has both of these images been affected by blurriness?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Has both of these images been affected by blurriness?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Has both of these images been affected by blurriness?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5514,[Response]: A.<|endoftext|>, [Correct Ans]: Much lower, , [Prog]: 963:  96%|▉| 964/1000 [10:
[Running Accuracy]: 0.5519,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 964:  96%|▉| 964/1000 [10:25<00:3
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Has both of these images been affected by blurriness?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the aesthetic composition of the second image?
A. much worse
B. almost the same
C. much worse
D. much better
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the aesthetic composition of the second image?
A. much worse
B. almost the same
C. much worse
D. much better
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the aesthetic composition of the second image?\nA. much worse\nB. almost the same\nC. much worse\nD. much better\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.5519,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 964:  96%|▉| 965/1000 [10:26<00:2
[Running Accuracy]: 0.5523,[Response]: D.<|endoftext|>, [Correct Ans]: much better, , [Prog]: 965:  96%|▉| 965/1000 [10
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the aesthetic composition of the second image?\nA. much worse\nB. almost the same\nC. much worse\nD. much better\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is there little difference in sharpness between these two images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is there little difference in sharpness between these two images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is there little difference in sharpness between these two images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5523,[Response]: D.<|endoftext|>, [Correct Ans]: much better, , [Prog]: 965:  97%|▉| 966/1000 [10
[Running Accuracy]: 0.5518,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 966:  97%|▉| 966/1000 [10:26<00:2
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is there little difference in sharpness between these two images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Please identify what kind of distortion is not present in these two images?
A. Blur
B. Overexposure
C. Underexposure
D. Noise
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Please identify what kind of distortion is not present in these two images?
A. Blur
B. Overexposure
C. Underexposure
D. Noise
Answer with the option's letter from the given choices directly.

prompts: [["Please identify what kind of distortion is not present in these two images?\nA. Blur\nB. Overexposure\nC. Underexposure\nD. Noise\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5518,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 966:  97%|▉| 967/1000 [10:27<00:2
[Running Accuracy]: 0.5522,[Response]: C.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 967:  97%|▉| 967/1000 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Please identify what kind of distortion is not present in these two images?\nA. Blur\nB. Overexposure\nC. Underexposure\nD. Noise\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how rich are the colors in the first image?
A. Similar
B. Much less colorful
C. Much more colorful
D. Extremely monotonous
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how rich are the colors in the first image?
A. Similar
B. Much less colorful
C. Much more colorful
D. Extremely monotonous
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how rich are the colors in the first image?\nA. Similar\nB. Much less colorful\nC. Much more colorful\nD. Extremely monotonous\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2812], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9688], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5522,[Response]: C.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 967:  97%|▉| 968/1000 [
[Running Accuracy]: 0.5517,[Response]: B.<|endoftext|>, [Correct Ans]: Much more colorful, , [Prog]: 968:  97%|▉| 968/1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how rich are the colors in the first image?\nA. Similar\nB. Much less colorful\nC. Much more colorful\nD. Extremely monotonous\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What distortion did not appear in these two images?
A. overexposure
B. noise
C. blur
D. underexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What distortion did not appear in these two images?
A. overexposure
B. noise
C. blur
D. underexposure
Answer with the option's letter from the given choices directly.

prompts: [["What distortion did not appear in these two images?\nA. overexposure\nB. noise\nC. blur\nD. underexposure\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0625], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.5517,[Response]: B.<|endoftext|>, [Correct Ans]: Much more colorful, , [Prog]: 968:  97%|▉| 969/1
[Running Accuracy]: 0.5511,[Response]: D.<|endoftext|>, [Correct Ans]: noise, , [Prog]: 969:  97%|▉| 969/1000 [10:29<00
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What distortion did not appear in these two images?\nA. overexposure\nB. noise\nC. blur\nD. underexposure\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. About the same
B. Much blurrier
C. Much clearer
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. About the same
B. Much blurrier
C. Much clearer
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. About the same\nB. Much blurrier\nC. Much clearer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5511,[Response]: D.<|endoftext|>, [Correct Ans]: noise, , [Prog]: 969:  97%|▉| 970/1000 [10:29<00
[Running Accuracy]: 0.5515,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 970:  97%|▉| 970/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. About the same\nB. Much blurrier\nC. Much clearer\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the second image, how would you rate the authenticity of the first image?
A. Similar
B. Not very authentic
C. Much more authentic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the second image, how would you rate the authenticity of the first image?
A. Similar
B. Not very authentic
C. Much more authentic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how would you rate the authenticity of the first image?\nA. Similar\nB. Not very authentic\nC. Much more authentic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5515,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 970:  97%|▉| 971/1000 
[Running Accuracy]: 0.5510,[Response]: B.<|endoftext|>, [Correct Ans]: Much more authentic, , [Prog]: 971:  97%|▉| 971/
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the second image, how would you rate the authenticity of the first image?\nA. Similar\nB. Not very authentic\nC. Much more authentic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the second image more blurry than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the second image more blurry than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the second image more blurry than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5510,[Response]: B.<|endoftext|>, [Correct Ans]: Much more authentic, , [Prog]: 971:  97%|▉| 972/
[Running Accuracy]: 0.5514,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 972:  97%|▉| 972/1000 [10:30<00:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the second image more blurry than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Have both of these images been affected by blur?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Have both of these images been affected by blur?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Have both of these images been affected by blur?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5514,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 972:  97%|▉| 973/1000 [10:31<00:1
[Running Accuracy]: 0.5519,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 973:  97%|▉| 973/1000 [10:31<00:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Have both of these images been affected by blur?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the texture detail of the second image more clearly visible?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the texture detail of the second image more clearly visible?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the texture detail of the second image more clearly visible?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1562], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5519,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 973:  97%|▉| 974/1000 [10:31<00:1
[Running Accuracy]: 0.5524,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 974:  97%|▉| 974/1000 [10:31<00:15
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the texture detail of the second image more clearly visible?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Relative to the first image, how is the composition of the second image?
A. About the same
B. Composition is worse
C. Composition is better
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Relative to the first image, how is the composition of the second image?
A. About the same
B. Composition is worse
C. Composition is better
Answer with the option's letter from the given choices directly.

prompts: [["Relative to the first image, how is the composition of the second image?\nA. About the same\nB. Composition is worse\nC. Composition is better\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5524,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 974:  98%|▉| 975/1000 [10:32<00:14
[Running Accuracy]: 0.5528,[Response]: C.<|endoftext|>, [Correct Ans]: Composition is better, , [Prog]: 975:  98%|▉| 97
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Relative to the first image, how is the composition of the second image?\nA. About the same\nB. Composition is worse\nC. Composition is better\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image sharper than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5528,[Response]: C.<|endoftext|>, [Correct Ans]: Composition is better, , [Prog]: 975:  98%|▉| 97
[Running Accuracy]: 0.5533,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 976:  98%|▉| 976/1000 [10:32<00:13
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image sharper than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how real is the second image?
A. More realistic
B. Less realistic
C. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how real is the second image?
A. More realistic
B. Less realistic
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how real is the second image?\nA. More realistic\nB. Less realistic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2188], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3594], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5533,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 976:  98%|▉| 977/1000 [10:33<00:14
[Running Accuracy]: 0.5537,[Response]: B.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 977:  98%|▉| 977/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how real is the second image?\nA. More realistic\nB. Less realistic\nC. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the color of the second image richer than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the color of the second image richer than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the second image richer than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5537,[Response]: B.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 977:  98%|▉| 978/1000 
[Running Accuracy]: 0.5542,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 978:  98%|▉| 978/1000 [10:34<00:1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the color of the second image richer than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Have both of these images been affected by noise?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Have both of these images been affected by noise?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Have both of these images been affected by noise?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2031], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.7500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5542,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 978:  98%|▉| 979/1000 [10:35<00:1
[Running Accuracy]: 0.5536,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 979:  98%|▉| 979/1000 [10:35<00:13
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Have both of these images been affected by noise?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the lighting of the second image, how is the lighting of the first image?
A. Slightly worse
B. Slightly better
C. Much worse
D. About the same
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the lighting of the second image, how is the lighting of the first image?
A. Slightly worse
B. Slightly better
C. Much worse
D. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the lighting of the second image, how is the lighting of the first image?\nA. Slightly worse\nB. Slightly better\nC. Much worse\nD. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.6875], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5536,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 979:  98%|▉| 980/1000 [10:35<00:13
[Running Accuracy]: 0.5531,[Response]: C.<|endoftext|>, [Correct Ans]: Slightly better, , [Prog]: 980:  98%|▉| 980/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the lighting of the second image, how is the lighting of the first image?\nA. Slightly worse\nB. Slightly better\nC. Much worse\nD. About the same\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the texture detail of the second image richer than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the texture detail of the second image richer than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the texture detail of the second image richer than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8906], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5531,[Response]: C.<|endoftext|>, [Correct Ans]: Slightly better, , [Prog]: 980:  98%|▉| 981/1000
[Running Accuracy]: 0.5535,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 981:  98%|▉| 981/1000 [10:36<00:11
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the texture detail of the second image richer than the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the lighting of the first image, how is the lighting of the second image?
A. Better
B. Weaker
C. Similar
D. Much weaker
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the lighting of the first image, how is the lighting of the second image?
A. Better
B. Weaker
C. Similar
D. Much weaker
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the lighting of the first image, how is the lighting of the second image?\nA. Better\nB. Weaker\nC. Similar\nD. Much weaker\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0781], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8125], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5535,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 981:  98%|▉| 982/1000 [10:36<00:10
[Running Accuracy]: 0.5540,[Response]: A.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 982:  98%|▉| 982/1000 [10:36<0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the lighting of the first image, how is the lighting of the second image?\nA. Better\nB. Weaker\nC. Similar\nD. Much weaker\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the lighting of the first image, how is the lighting of the second image?
A. Similar
B. Slightly better
C. Slightly worse
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the lighting of the first image, how is the lighting of the second image?
A. Similar
B. Slightly better
C. Slightly worse
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the lighting of the first image, how is the lighting of the second image?\nA. Similar\nB. Slightly better\nC. Slightly worse\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5540,[Response]: A.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 982:  98%|▉| 983/1000 [10:37<0
[Running Accuracy]: 0.5534,[Response]: C.<|endoftext|>, [Correct Ans]: Slightly better, , [Prog]: 983:  98%|▉| 983/1000
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the lighting of the first image, how is the lighting of the second image?\nA. Similar\nB. Slightly better\nC. Slightly worse\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What is the distortion that does not appear in these two images?
A. Noise
B. Motion blur
C. Overexposure
D. Underexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What is the distortion that does not appear in these two images?
A. Noise
B. Motion blur
C. Overexposure
D. Underexposure
Answer with the option's letter from the given choices directly.

prompts: [["What is the distortion that does not appear in these two images?\nA. Noise\nB. Motion blur\nC. Overexposure\nD. Underexposure\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2344], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 D.

[Running Accuracy]: 0.5534,[Response]: C.<|endoftext|>, [Correct Ans]: Slightly better, , [Prog]: 983:  98%|▉| 984/1000
[Running Accuracy]: 0.5528,[Response]: D.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 984:  98%|▉| 984/1000 [10
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What is the distortion that does not appear in these two images?\nA. Noise\nB. Motion blur\nC. Overexposure\nD. Underexposure\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'D.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Have both of these images been affected by noise?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Have both of these images been affected by noise?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Have both of these images been affected by noise?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5528,[Response]: D.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 984:  98%|▉| 985/1000 [10
[Running Accuracy]: 0.5523,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 985:  98%|▉| 985/1000 [10:38<00:08
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Have both of these images been affected by noise?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are both images very clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are both images very clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both images very clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5523,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 985:  99%|▉| 986/1000 [10:39<00:07
[Running Accuracy]: 0.5527,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 986:  99%|▉| 986/1000 [10:39<00:07
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are both images very clear?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the compositions of these two images both very good?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the compositions of these two images both very good?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the compositions of these two images both very good?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2656], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5527,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 986:  99%|▉| 987/1000 [10:39<00:07
[Running Accuracy]: 0.5532,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 987:  99%|▉| 987/1000 [10:39<00:07
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the compositions of these two images both very good?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which image is more severely affected by motion blur?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which image is more severely affected by motion blur?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.

prompts: [["Which image is more severely affected by motion blur?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5532,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 987:  99%|▉| 988/1000 [10:40<00:06
[Running Accuracy]: 0.5536,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 988:  99%|▉| 988/1000 [1
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which image is more severely affected by motion blur?\nA. Second image\nB. First image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which area is more affected by blurring?
A. The focused red flowers in the second image
B. The flower bush background in the second image
C. The background in the first image
D. The man's silhouette in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which area is more affected by blurring?
A. The focused red flowers in the second image
B. The flower bush background in the second image
C. The background in the first image
D. The man's silhouette in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more affected by blurring?\nA. The focused red flowers in the second image\nB. The flower bush background in the second image\nC. The background in the first image\nD. The man's silhouette in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.4062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9062], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5536,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 988:  99%|▉| 989/1000 [1
[Running Accuracy]: 0.5541,[Response]: B.<|endoftext|>, [Correct Ans]: The flower bush background in the second image, 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which area is more affected by blurring?\nA. The focused red flowers in the second image\nB. The flower bush background in the second image\nC. The background in the first image\nD. The man's silhouette in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. Butterfly in the first image
B. Sky in the second image
C. Ground in the second image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. Butterfly in the first image
B. Sky in the second image
C. Ground in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. Butterfly in the first image\nB. Sky in the second image\nC. Ground in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.6406], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.9844], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5541,[Response]: B.<|endoftext|>, [Correct Ans]: The flower bush background in the second image, 
[Running Accuracy]: 0.5545,[Response]: B.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 990:  99%|▉| 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. Butterfly in the first image\nB. Sky in the second image\nC. Ground in the second image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part is most severely affected by mosaic distortion?
A. Background of the first image
B. Cabinet of the second image
C. Hand of the baby in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part is most severely affected by mosaic distortion?
A. Background of the first image
B. Cabinet of the second image
C. Hand of the baby in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most severely affected by mosaic distortion?\nA. Background of the first image\nB. Cabinet of the second image\nC. Hand of the baby in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5545,[Response]: B.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 990:  99%|▉| 
[Running Accuracy]: 0.5540,[Response]: A.<|endoftext|>, [Correct Ans]: Cabinet of the second image, , [Prog]: 991:  99%
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part is most severely affected by mosaic distortion?\nA. Background of the first image\nB. Cabinet of the second image\nC. Hand of the baby in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the face of the person in the first image less clear than the face of the person in the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the face of the person in the first image less clear than the face of the person in the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the face of the person in the first image less clear than the face of the person in the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5312], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5540,[Response]: A.<|endoftext|>, [Correct Ans]: Cabinet of the second image, , [Prog]: 991:  99%
[Running Accuracy]: 0.5534,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 992:  99%|▉| 992/1000 [10:42<00:0
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the face of the person in the first image less clear than the face of the person in the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how does the reality of the second image compare?
A. Less realistic
B. About the same
C. More realistic
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how does the reality of the second image compare?
A. Less realistic
B. About the same
C. More realistic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how does the reality of the second image compare?\nA. Less realistic\nB. About the same\nC. More realistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.4219], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5534,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 992:  99%|▉| 993/1000 [10:43<00:0
[Running Accuracy]: 0.5529,[Response]: A.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 993:  99%|▉| 993/1000 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how does the reality of the second image compare?\nA. Less realistic\nB. About the same\nC. More realistic\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most affected by motion blur?
A. The wire mesh in the second image
B. The wolf in the second image
C. The pattern in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most affected by motion blur?
A. The wire mesh in the second image
B. The wolf in the second image
C. The pattern in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most affected by motion blur?\nA. The wire mesh in the second image\nB. The wolf in the second image\nC. The pattern in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9531], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8281], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5529,[Response]: A.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 993:  99%|▉| 994/1000 
[Running Accuracy]: 0.5533,[Response]: B.<|endoftext|>, [Correct Ans]: The wolf in the second image, , [Prog]: 994:  99
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most affected by motion blur?\nA. The wire mesh in the second image\nB. The wolf in the second image\nC. The pattern in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Which part below is most severely affected by overexposure?
A. The leaves in the first image
B. The zebra in the second image
C. The light source in the first image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which part below is most severely affected by overexposure?
A. The leaves in the first image
B. The zebra in the second image
C. The light source in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by overexposure?\nA. The leaves in the first image\nB. The zebra in the second image\nC. The light source in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.2969], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5533,[Response]: B.<|endoftext|>, [Correct Ans]: The wolf in the second image, , [Prog]: 994: 100
[Running Accuracy]: 0.5538,[Response]: C.<|endoftext|>, [Correct Ans]: The light source in the first image, , [Prog]: 9
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Which part below is most severely affected by overexposure?\nA. The leaves in the first image\nB. The zebra in the second image\nC. The light source in the first image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>No distortion appears in these two images?
A. Lens flare
B. Overexposure
C. Motion blur
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:No distortion appears in these two images?
A. Lens flare
B. Overexposure
C. Motion blur
Answer with the option's letter from the given choices directly.

prompts: [["No distortion appears in these two images?\nA. Lens flare\nB. Overexposure\nC. Motion blur\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.1719], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5538,[Response]: C.<|endoftext|>, [Correct Ans]: The light source in the first image, , [Prog]: 9
[Running Accuracy]: 0.5532,[Response]: C.<|endoftext|>, [Correct Ans]: Lens flare, , [Prog]: 996: 100%|▉| 996/1000 [10:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>No distortion appears in these two images?\nA. Lens flare\nB. Overexposure\nC. Motion blur\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>What kind of distortion is not present in these two images?
A. Motion blur
B. Overexposure
C. Ghosting
D. False image
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What kind of distortion is not present in these two images?
A. Motion blur
B. Overexposure
C. Ghosting
D. False image
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion is not present in these two images?\nA. Motion blur\nB. Overexposure\nC. Ghosting\nD. False image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.0469], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5532,[Response]: C.<|endoftext|>, [Correct Ans]: Lens flare, , [Prog]: 996: 100%|▉| 997/1000 [10:
[Running Accuracy]: 0.5527,[Response]: B.<|endoftext|>, [Correct Ans]: Ghosting, , [Prog]: 997: 100%|▉| 997/1000 [10:45
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>What kind of distortion is not present in these two images?\nA. Motion blur\nB. Overexposure\nC. Ghosting\nD. False image\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Are the colors of these two images both not rich?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Are the colors of these two images both not rich?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the colors of these two images both not rich?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1094], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.3438], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5527,[Response]: B.<|endoftext|>, [Correct Ans]: Ghosting, , [Prog]: 997: 100%|▉| 998/1000 [10:46
[Running Accuracy]: 0.5531,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 998: 100%|▉| 998/1000 [10:46<00:01
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Are the colors of these two images both not rich?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Is the first image blurrier than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Is the first image blurrier than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image blurrier than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9375], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-31.5000], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5531,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 998: 100%|▉| 999/1000 [10:47<00:00
[Running Accuracy]: 0.5536,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 999: 100%|▉| 999/1000 [10:47<00:00
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Is the first image blurrier than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>
The second image: <image>Compared to the first image, how is the clarity of the second image?
A. Comparable
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Compared to the first image, how is the clarity of the second image?
A. Comparable
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the clarity of the second image?\nA. Comparable\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
alpha tensor([-30.8750], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 C.

[Running Accuracy]: 0.5536,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 999: 100%|█| 1000/1000 [10:47<00:0
[Running Accuracy]: 0.5540,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 1000: 100%|█| 1000/1000 [10:
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: The first image: <image>\nThe second image: <image>Compared to the first image, how is the clarity of the second image?\nA. Comparable\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'C.<|endoftext|>'} 


[Running Accuracy]: 0.5540,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 1000: 100%|█| 1000/1000 [10: