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 [00:04<00:04,  4.17s/it]
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████| 2/2 [00:04<00:00,  2.20s/it]
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████| 2/2 [00:04<00:00,  2.49s/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%|                                                                                         | 0/999 [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 lighting in the second image?
A. More sufficient
B. Similar
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. More sufficient
B. Similar
C. Less sufficient
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 lighting in the second image?\nA. More sufficient\nB. Similar\nC. Less sufficient\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])
 C.

  0%|                                                                                 | 1/999 [00:01<25:23,  1.53s/it]
[Running Accuracy]: 1.0000,[Response]: C.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 1:   0%| | 1/999 [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 in the second image?\nA. More sufficient\nB. Similar\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 lighting in the second 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:Compared to the first image, how is the lighting in the second image?
A. More sufficient
B. About the same
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. More sufficient\nB. About the same\nC. Less sufficient\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([-31.2344], 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]: 1.0000,[Response]: C.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 1:   0%| | 2/999 [00
[Running Accuracy]: 0.5000,[Response]: C.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 2:   0%| | 2/999 [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 in the second 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>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([-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])
 B.

[Running Accuracy]: 0.5000,[Response]: C.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 2:   0%| | 3/999 [00
[Running Accuracy]: 0.3333,[Response]: B.<|endoftext|>, [Correct Ans]: Second Image, , [Prog]: 3:   0%| | 3/999 [00:03
 {'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>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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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([-31.3906], 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([-30.9062], 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]: 0.3333,[Response]: B.<|endoftext|>, [Correct Ans]: Second Image, , [Prog]: 3:   0%| | 4/999 [00:04
[Running Accuracy]: 0.5000,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 4:   0%| | 4/999 [00:04<16:19,  
 {'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 very 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>How does the clarity of the second image compare to the first 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:How does the clarity of the second image compare to the first image?
A. Clearer
B. About the same
C. More blurry
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. Clearer\nB. About the same\nC. More blurry\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([-30.5469], 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]: 0.5000,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 4:   1%| | 5/999 [00:05<16:00,  
[Running Accuracy]: 0.4000,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 5:   1%| | 5/999 [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>How does the clarity of the second image compare to the first image?\nA. Clearer\nB. About the same\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>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([-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])
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]: 0.4000,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 5:   1%| | 6/999 [00:
[Running Accuracy]: 0.5000,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 6:   1%| | 6/999 [00:05<
 {'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 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>How does the sharpness of the second image compare to the first image?
A. Sharper
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:How does the sharpness of the second image compare to the first image?
A. Sharper
B. About the same
C. More blurry
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. Sharper\nB. About the same\nC. More blurry\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]: 0.5000,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 6:   1%| | 7/999 [00:06<
[Running Accuracy]: 0.5714,[Response]: C.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 7:   1%| | 7/999 [00:06<
 {'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 does the sharpness of the second image compare to the first image?\nA. Sharper\nB. About the same\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 authentic is 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 authentic is 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 authentic is 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([-30.9688], 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([-31.0938], 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]: 0.5714,[Response]: C.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 7:   1%| | 8/999 [00:07<
[Running Accuracy]: 0.6250,[Response]: C.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 8:   1%| | 8/999 [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 authentic is 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>Neither of the following two images has any distortion?
A. Noise
B. Motion blur
C. Underexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Neither of the following two images has any distortion?
A. Noise
B. Motion blur
C. Underexposure
Answer with the option's letter from the given choices directly.

prompts: [["Neither of the following two images has any distortion?\nA. Noise\nB. Motion blur\nC. Underexposure\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([-30.9531], 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]: 0.6250,[Response]: C.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 8:   1%| | 9/999 [00:
[Running Accuracy]: 0.5556,[Response]: B.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 9:   1%| | 9/999 [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>Neither of the following two images has any distortion?\nA. Noise\nB. Motion blur\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 does 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 does 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 does 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([-31.0312], 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([-30.8750], 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]: 0.5556,[Response]: B.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 9:   1%| | 10/999 [00:
[Running Accuracy]: 0.6000,[Response]: A.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 10:   1%| | 10/999 [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 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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([-31.0938], 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([-31.0312], 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]: 0.6000,[Response]: A.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 10:   1%| | 11/999 [0
[Running Accuracy]: 0.5455,[Response]: A.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 11:   1%| | 11/999 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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>Compared to the first image, how clear is 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 clear is 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 clear is 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([-30.9062], 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([-31.2188], 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]: 0.5455,[Response]: A.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 11:   1%| | 12/999 [
[Running Accuracy]: 0.5833,[Response]: C.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 12:   1%| | 12/999 [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>Compared to the first image, how clear is 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': '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 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 blurry?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images blurry?\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([-30.7812], 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]: 0.5833,[Response]: C.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 12:   1%| | 13/999 [00:1
[Running Accuracy]: 0.6154,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 13:   1%| | 13/999 [00:10<11:06,
 {'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 both of these images 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>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([-31.3438], 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([-30.8750], 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]: 0.6154,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 13:   1%| | 14/999 [00:11<10:37,
[Running Accuracy]: 0.6429,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 14:   1%| | 14/999 [00:11<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 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>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([-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([-30.5469], 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]: 0.6429,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 14:   2%| | 15/999 [00:12<1
[Running Accuracy]: 0.6000,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 15:   2%| | 15/999 [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 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>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([-31.0312], 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([-30.7031], 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]: 0.6000,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 15:   2%| | 16/999 [00:1
[Running Accuracy]: 0.6250,[Response]: A.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 16:   2%| | 16/999 [00:12<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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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 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 is 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 is 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([-31.4375], 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([-31.2031], 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]: 0.6250,[Response]: A.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 16:   2%| | 17/999 [00:13<10
[Running Accuracy]: 0.6471,[Response]: B.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 17:   2%| | 17/999 [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. 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>Compared to the first image, how does the authenticity 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 authenticity 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 authenticity 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([-31.3438], 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]: 0.6471,[Response]: B.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 17:   2%| | 18/999 [0
[Running Accuracy]: 0.6667,[Response]: A.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 18:   2%| | 18/999 [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 authenticity 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>Compared to the first image, how is the illumination in the second 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:Compared to the first image, how is the illumination in the second image?
A. More sufficient
B. About the same
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. More sufficient\nB. About the same\nC. Less 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([-31.2344], 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]: 0.6667,[Response]: A.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 18:   2%| | 19/999 [0
[Running Accuracy]: 0.6316,[Response]: C.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 19:   2%| | 19/999 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. 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>Which part below is most severely affected by overexposure?
A. The bird in the first image
B. The shop sign in the second image
C. The balloon 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 bird in the first image
B. The shop sign in the second image
C. The balloon 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 bird in the first image\nB. The shop sign in the second image\nC. The balloon 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([-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]: 0.6316,[Response]: C.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 19:   2%| | 20/999 [
[Running Accuracy]: 0.6000,[Response]: C.<|endoftext|>, [Correct Ans]: The shop sign in the second image, , [Prog]: 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>Which part below is most severely affected by overexposure?\nA. The bird in the first image\nB. The shop sign in the second image\nC. The balloon 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([-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([-31.4688], 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]: 0.6000,[Response]: C.<|endoftext|>, [Correct Ans]: The shop sign in the second image, , [Prog]: 20
[Running Accuracy]: 0.6190,[Response]: A.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 21:   2%| | 21/999 [00:16<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. Clearer\nB. Blurrier\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 sharpness of the second image?
A. Blurrier
B. Sharper
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. Blurrier
B. Sharper
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. Blurrier\nB. Sharper\nC. Similar\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([-31.3906], 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]: 0.6190,[Response]: A.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 21:   2%| | 22/999 [00:16<10
[Running Accuracy]: 0.6364,[Response]: B.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 22:   2%| | 22/999 [00:16<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. Blurrier\nB. Sharper\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. 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([-31.0469], 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])
 B.

[Running Accuracy]: 0.6364,[Response]: B.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 22:   2%| | 23/999 [00:17<10
[Running Accuracy]: 0.6522,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 23:   2%| | 23/999 [00:17<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. 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 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([-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]: 0.6522,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 23:   2%| | 24/999 [00:17<10
[Running Accuracy]: 0.6250,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 24:   2%| | 24/999 [00:17<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 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 richness of colors in the second image?
A. Similar
B. More monotonous
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. More monotonous
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. More monotonous\nC. More rich\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([-30.7812], 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]: 0.6250,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 24:   3%| | 25/999 [00:18<10:26, 
[Running Accuracy]: 0.6400,[Response]: B.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 25:   3%| | 25/999 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. More monotonous\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>Which image 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 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 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([-29.3125], 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([-30.8438], 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]: 0.6400,[Response]: B.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 25:   3%| | 26/999 [
[Running Accuracy]: 0.6154,[Response]: B.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 26:   3%| | 26/999 [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 overexposure?\nA. Second image\nB. 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 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([-30.7500], 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([-31.2031], 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]: 0.6154,[Response]: B.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 26:   3%| | 27/999 [00:
[Running Accuracy]: 0.5926,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 27:   3%| | 27/999 [00:20<11:15,
 {'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 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>Are the colors 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 colors of these two images both rich?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the colors of these two images both rich?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.3906], 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([-31.1250], 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]: 0.5926,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 27:   3%| | 28/999 [00:20<10:45,
[Running Accuracy]: 0.6071,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 28:   3%| | 28/999 [00:20<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>Are the colors 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 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([-31.1406], 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([-31.0469], 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]: 0.6071,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 28:   3%| | 29/999 [00:21<10:21,
[Running Accuracy]: 0.5862,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 29:   3%| | 29/999 [00:21<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>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 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([-31.0312], 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]: 0.5862,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 29:   3%| | 30/999 [00:22<11:26, 
[Running Accuracy]: 0.6000,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 30:   3%| | 30/999 [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>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([-30.7812], 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([-31.0469], 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]: 0.6000,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 30:   3%| | 31/999 [00:
[Running Accuracy]: 0.5806,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 31:   3%| | 31/999 [00:22<10:55, 
 {'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>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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 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 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 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([-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([-31.2344], 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]: 0.5806,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 31:   3%| | 32/999 [00:23<10:41, 
[Running Accuracy]: 0.5625,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 32:   3%| | 32/999 [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>Which image 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>Are the lighting conditions sufficient for 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 the lighting conditions sufficient for both of these images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the lighting conditions sufficient for both of these images?\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([-31.2344], 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]: 0.5625,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 32:   3%| | 33/999 [00:2
[Running Accuracy]: 0.5758,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 33:   3%| | 33/999 [00:24<11:50, 
 {'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 the lighting conditions sufficient for both of these 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>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([-31.5312], 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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.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]: No, , [Prog]: 33:   3%| | 34/999 [00:24<11:21, 
[Running Accuracy]: 0.5588,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 34:   3%| | 34/999 [00:24<11:21,
 {'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 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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([-31.6562], 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([-30.8125], 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]: 0.5588,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 34:   4%| | 35/999 [00:25<10:58,
[Running Accuracy]: 0.5714,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 35:   4%| | 35/999 [00:25<10:58, 
 {'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 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 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([-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]: 0.5714,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 35:   4%| | 36/999 [00:26<12:05, 
[Running Accuracy]: 0.5833,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 36:   4%| | 36/999 [00:26<12:05,
 {'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 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 illumination of 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 illumination of 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 illumination of 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([-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([-31.4844], 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]: 0.5833,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 36:   4%| | 37/999 [00:27<11:27,
[Running Accuracy]: 0.5676,[Response]: B.<|endoftext|>, [Correct Ans]: more sufficient, , [Prog]: 37:   4%| | 37/999 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 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>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([-31.2031], 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([-31.4062], 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]: 0.5676,[Response]: B.<|endoftext|>, [Correct Ans]: more sufficient, , [Prog]: 37:   4%| | 38/999 [
[Running Accuracy]: 0.5526,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 38:   4%| | 38/999 [00:27<11:49,
 {'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>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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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([-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([-30.3438], 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]: 0.5526,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 38:   4%| | 39/999 [00:28<11:15,
[Running Accuracy]: 0.5641,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 39:   4%| | 39/999 [00:28<11:15, 
 {'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>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>Is the first image more unreal 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 unreal 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 unreal 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([-31.2656], 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]: 0.5641,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 39:   4%| | 40/999 [00:29<10:40, 
[Running Accuracy]: 0.5500,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 40:   4%| | 40/999 [00:29<10:40,
 {'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 more unreal 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 image below 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 below 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 below 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([-31.1875], 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([-31.0469], 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]: 0.5500,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 40:   4%| | 41/999 [00:29<10:28,
[Running Accuracy]: 0.5366,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 41:   4%| | 41/999 [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>Which image below 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>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([-30.1406], 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([-30.7188], 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]: 0.5366,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 41:   4%| | 42/999 [00:3
[Running Accuracy]: 0.5238,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 42:   4%| | 42/999 [00:30<10:09,
 {'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 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 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([-30.6406], 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]: 0.5238,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 42:   4%| | 43/999 [00:30<09:58,
[Running Accuracy]: 0.5116,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 43:   4%| | 43/999 [00:30<09:58,
 {'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>Are both of these images not of 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 both of these images not of high clarity?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images not of high clarity?\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([-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])
 B.

[Running Accuracy]: 0.5116,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 43:   4%| | 44/999 [00:31<09:52,
[Running Accuracy]: 0.5227,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 44:   4%| | 44/999 [00:31<09:52,
 {'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 both of these images not of high clarity?\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 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([-31.2812], 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]: 0.5227,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 44:   5%| | 45/999 [00:32<10:31,
[Running Accuracy]: 0.5333,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 45:   5%| | 45/999 [00:32<10:31, 
 {'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 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 more severely affected by overexposure?
A. The vehicle in the second image
B. The crystal 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 more severely affected by overexposure?
A. The vehicle in the second image
B. The crystal 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 more severely affected by overexposure?\nA. The vehicle in the second image\nB. The crystal 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([-31.4219], 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([-30.8906], 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])
 C.

[Running Accuracy]: 0.5333,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 45:   5%| | 46/999 [00:33<11:41, 
[Running Accuracy]: 0.5435,[Response]: C.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 46:   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 more severely affected by overexposure?\nA. The vehicle in the second image\nB. The crystal 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>Which image below 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 below looks more realistic?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.

prompts: [["Which image below looks more realistic?\nA. Second image\nB. 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])
 A.

[Running Accuracy]: 0.5435,[Response]: C.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 46:   5%
[Running Accuracy]: 0.5532,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 47:   5%| | 47/999 [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 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>Which image is more seriously affected by snowflake 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 more seriously affected by snowflake noise?
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 snowflake noise?\nA. Second image\nB. First image\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([-31.2188], 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]: 0.5532,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 47:   5%| | 48/999 [00:
[Running Accuracy]: 0.5625,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 48:   5%| | 48/999 [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 seriously affected by snowflake 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>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([-30.3438], 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]: 0.5625,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 48:   5%| | 49/999 [00:
[Running Accuracy]: 0.5510,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 49:   5%| | 49/999 [00:35<12:01,
 {'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 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. 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([-31.3281], 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([-30.9688], 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]: 0.5510,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 49:   5%| | 50/999 [00:36<11:24,
[Running Accuracy]: 0.5400,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 50:   5%| | 50/999 [00:36<11:24,
 {'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 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([-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([-30.8281], 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]: 0.5400,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 50:   5%| | 51/999 [00:36<10:56,
[Running Accuracy]: 0.5490,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 51:   5%| | 51/999 [00:36<10:56, 
 {'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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 blurry?
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 blurry?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.

prompts: [["Which image is more blurry?\nA. Second image\nB. 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([-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]: 0.5490,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 51:   5%| | 52/999 [00:37<10:34, 
[Running Accuracy]: 0.5385,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 52:   5%| | 52/999 [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 image is more blurry?\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 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([-31.1562], 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])
 A.

[Running Accuracy]: 0.5385,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 52:   5%| | 53/999 [00:3
[Running Accuracy]: 0.5472,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 53:   5%| | 53/999 [00:38<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>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 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([-30.5781], 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([-30.6094], 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])
 A.

[Running Accuracy]: 0.5472,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 53:   5%| | 54/999 [00:38<10:02,
[Running Accuracy]: 0.5370,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 54:   5%| | 54/999 [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 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>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([-31.1094], 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([-30.0312], 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]: 0.5370,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 54:   6%| | 55/999 [00:3
[Running Accuracy]: 0.5273,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 55:   6%| | 55/999 [00:39<09:50,
 {'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>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. 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([-31.3750], 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([-31.0781], 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]: 0.5273,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 55:   6%| | 56/999 [00:40<11:57,
[Running Accuracy]: 0.5357,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 56:   6%| | 56/999 [00:40<11:57,
 {'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. 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 is the distortion that does not appear in the two images?
A. overexposure
B. underexposure
C. motion blur
D. low light
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. underexposure
C. motion blur
D. low light
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. underexposure\nC. motion blur\nD. low light\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([-30.7031], 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])
 B.

[Running Accuracy]: 0.5357,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 56:   6%| | 57/999 [00:41<12:01,
[Running Accuracy]: 0.5263,[Response]: B.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 57:   6%| | 57/999 [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 is the distortion that does not appear in the two images?\nA. overexposure\nB. underexposure\nC. motion blur\nD. low 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>Compared to the second image, how rich are the colors in the first image?
A. More desolate color
B. More rich color
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 rich are the colors in the first image?
A. More desolate color
B. More rich color
C. About the same
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. More desolate color\nB. More rich color\nC. About the same\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([-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]: 0.5263,[Response]: B.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 57:   6%| | 58/999 [00:
[Running Accuracy]: 0.5345,[Response]: B.<|endoftext|>, [Correct Ans]: More rich color, , [Prog]: 58:   6%| | 58/999 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. More desolate color\nB. More rich color\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([-31.1094], 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])
 A.

[Running Accuracy]: 0.5345,[Response]: B.<|endoftext|>, [Correct Ans]: More rich color, , [Prog]: 58:   6%| | 59/999 [
[Running Accuracy]: 0.5254,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 59:   6%| | 59/999 [00:42<13:05,
 {'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 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 second image significantly stronger 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 significantly stronger 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 significantly stronger than the first 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([-30.9219], 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]: 0.5254,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 59:   6%| | 60/999 [00:43<13:37,
[Running Accuracy]: 0.5333,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 60:   6%| | 60/999 [00:43<13:37,
 {'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 lighting of the second image significantly stronger 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 second image, how is the focusing of the first 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 second image, how is the focusing of the first image?
A. Similar
B. Better
C. Worse
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. Similar\nB. Better\nC. Worse\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([-31.0469], 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]: 0.5333,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 60:   6%| | 61/999 [00:44<12:58,
[Running Accuracy]: 0.5410,[Response]: C.<|endoftext|>, [Correct Ans]: Worse, , [Prog]: 61:   6%| | 61/999 [00:44<12:5
 {'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 focusing of the first 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>How does the lighting condition of the first image compared to the second 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 of the first image compared to the second 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 of the first image compared to the second 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([-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([-31.1406], 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]: 0.5410,[Response]: C.<|endoftext|>, [Correct Ans]: Worse, , [Prog]: 61:   6%| | 62/999 [00:45<15:0
[Running Accuracy]: 0.5484,[Response]: A.<|endoftext|>, [Correct Ans]: Slightly worse, , [Prog]: 62:   6%| | 62/999 [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 condition of the first image compared to the second 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 these two images is not affected by motion blur?
A. The person's hand in the second image
B. The bicycle pedal in the first image
C. The background in the first image
D. The bicycle wheel 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 in these two images is not affected by motion blur?
A. The person's hand in the second image
B. The bicycle pedal in the first image
C. The background in the first image
D. The bicycle wheel in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area in these two images is not affected by motion blur?\nA. The person's hand in the second image\nB. The bicycle pedal in the first image\nC. The background in the first image\nD. The bicycle wheel in the first image\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([-31.4375], 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]: 0.5484,[Response]: A.<|endoftext|>, [Correct Ans]: Slightly worse, , [Prog]: 62:   6%| | 63/999 [0
[Running Accuracy]: 0.5556,[Response]: A.<|endoftext|>, [Correct Ans]: The person's hand 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 in these two images is not affected by motion blur?\nA. The person's hand in the second image\nB. The bicycle pedal in the first image\nC. The background in the first image\nD. The bicycle wheel 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 lighting condition of the first image?
A. Better
B. Worse
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. Better
B. Worse
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. Better\nB. Worse\nC. Similar\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([-31.0312], 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]: 0.5556,[Response]: A.<|endoftext|>, [Correct Ans]: The person's hand in the second image, , [Prog]
[Running Accuracy]: 0.5469,[Response]: B.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 64:   6%| | 64/999 [00:47<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 second image, how is the lighting condition of the first image?\nA. Better\nB. Worse\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 lighting of the second image?
A. Better
B. Similar
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 lighting of the second image?
A. Better
B. Similar
C. Worse
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. Better\nB. Similar\nC. Worse\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]: 0.5469,[Response]: B.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 64:   7%| | 65/999 [00:49<16:
[Running Accuracy]: 0.5385,[Response]: C.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 65:   7%| | 65/999 [00:49<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 lighting of the second image?\nA. Better\nB. Similar\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 there motion blur 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 in these two images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is there motion blur in these two images?\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([-30.5469], 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]: 0.5385,[Response]: C.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 65:   7%| | 66/999 [00:49<15:
[Running Accuracy]: 0.5455,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 66:   7%| | 66/999 [00:49<15:28,
 {'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 there motion blur 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>Is overexposure present 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 overexposure present in these two images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is overexposure present in these two images?\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])
 B.

[Running Accuracy]: 0.5455,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 66:   7%| | 67/999 [00:50<15:25,
[Running Accuracy]: 0.5522,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 67:   7%| | 67/999 [00:50<15:25,
 {'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>Is overexposure present 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 second image, how is the composition of the first image?
A. more beautiful
B. about the same
C. less beautiful
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. more beautiful
B. about the same
C. less beautiful
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. more beautiful\nB. about the same\nC. less beautiful\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([-30.7188], 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]: 0.5522,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 67:   7%| | 68/999 [00:51<15:27,
[Running Accuracy]: 0.5441,[Response]: B.<|endoftext|>, [Correct Ans]: less beautiful, , [Prog]: 68:   7%| | 68/999 [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 composition of the first image?\nA. more beautiful\nB. about the same\nC. less beautiful\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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. Motion Blur
B. Blur
C. Underexposed
D. Overexposed
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. Motion Blur
B. Blur
C. Underexposed
D. Overexposed
Answer with the option's letter from the given choices directly.

prompts: [["Which kind of distortion is not present in these two images?\nA. Motion Blur\nB. Blur\nC. Underexposed\nD. Overexposed\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])
 D.

[Running Accuracy]: 0.5441,[Response]: B.<|endoftext|>, [Correct Ans]: less beautiful, , [Prog]: 68:   7%| | 69/999 [0
[Running Accuracy]: 0.5362,[Response]: D.<|endoftext|>, [Correct Ans]: Motion Blur, , [Prog]: 69:   7%| | 69/999 [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>Which kind of distortion is not present in these two images?\nA. Motion Blur\nB. Blur\nC. Underexposed\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>Which area in the two images is not affected by underexposure?
A. The wooden house in the second image
B. The fire hydrant in the first image
C. The tree trunk in the second image
D. The tree 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 area in the two images is not affected by underexposure?
A. The wooden house in the second image
B. The fire hydrant in the first image
C. The tree trunk in the second image
D. The tree leaves 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 underexposure?\nA. The wooden house in the second image\nB. The fire hydrant in the first image\nC. The tree trunk in the second image\nD. The tree leaves 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([-31.3125], 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]: 0.5362,[Response]: D.<|endoftext|>, [Correct Ans]: Motion Blur, , [Prog]: 69:   7%| | 70/999 [00:5
[Running Accuracy]: 0.5429,[Response]: B.<|endoftext|>, [Correct Ans]: The fire hydrant 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 area in the two images is not affected by underexposure?\nA. The wooden house in the second image\nB. The fire hydrant in the first image\nC. The tree trunk in the second image\nD. The tree leaves 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 fine texture of the second image clearer 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 fine texture of the second image clearer than that of the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the fine texture of the second image clearer than that of the first image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], 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([-30.4219], 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]: 0.5429,[Response]: B.<|endoftext|>, [Correct Ans]: The fire hydrant in the first image, , [Prog]: 
[Running Accuracy]: 0.5352,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 71:   7%| | 71/999 [00:55<15:50,
 {'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>Is the fine texture of the second image clearer than that of 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 of the following distortions did not occur in the two images?
A. Overexposure
B. Blur
C. Underexposure
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 occur in the two images?
A. Overexposure
B. Blur
C. Underexposure
D. Motion blur
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following distortions did not occur in the two images?\nA. Overexposure\nB. Blur\nC. Underexposure\nD. Motion blur\nAnswer with the option's letter from the given choices directly.\n"]]
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])
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]: 0.5352,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 71:   7%| | 72/999 [00:56<15:19,
[Running Accuracy]: 0.5278,[Response]: C.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 72:   7%| | 72/999 [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>Which of the following distortions did not occur in the two images?\nA. Overexposure\nB. Blur\nC. Underexposure\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>Which of the following distortions does not appear in the two images?
A. Underexposure
B. Motion blur
C. Low light
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 distortions does not appear in the two images?
A. Underexposure
B. Motion blur
C. Low light
D. Noise
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. Underexposure\nB. Motion blur\nC. Low light\nD. Noise\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([-30.9688], 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]: 0.5278,[Response]: C.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 72:   7%| | 73/999 [00:5
[Running Accuracy]: 0.5205,[Response]: A.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 73:   7%| | 73/999 [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>Which of the following distortions does not appear in the two images?\nA. Underexposure\nB. Motion blur\nC. Low light\nD. 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 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([-30.8750], 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([-30.7500], 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])
 B.

[Running Accuracy]: 0.5205,[Response]: A.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 73:   7%| | 74/999 [00:5
[Running Accuracy]: 0.5270,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 74:   7%| | 74/999 [00:57<14:16, 
 {'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 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 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([-31.0469], 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([-31.1875], 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]: 0.5270,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 74:   8%| | 75/999 [00:58<12:54, 
[Running Accuracy]: 0.5200,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 75:   8%| | 75/999 [00:58<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 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>Which area in the two images is most affected by overexposure?
A. The window in the second image
B. The sky in the first image
C. The floor 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 in the two images is most affected by overexposure?
A. The window in the second image
B. The sky in the first image
C. The floor 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 in the two images is most affected by overexposure?\nA. The window in the second image\nB. The sky in the first image\nC. The floor 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([-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([-31.2969], 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]: 0.5200,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 75:   8%| | 76/999 [00:59<12:54,
[Running Accuracy]: 0.5263,[Response]: B.<|endoftext|>, [Correct Ans]: The sky in the first image, , [Prog]: 76:   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 in the two images is most affected by overexposure?\nA. The window in the second image\nB. The sky in the first image\nC. The floor 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>Is the color of the second image richer than 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 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 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([-31.1562], 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]: 0.5263,[Response]: B.<|endoftext|>, [Correct Ans]: The sky in the first image, , [Prog]: 76:   8%|
[Running Accuracy]: 0.5325,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 77:   8%| | 77/999 [01:00<13:02, 
 {'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 color of the second image richer than 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 second image more vibrant 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 more vibrant 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 more vibrant than the first image?\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([-31.1719], 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]: 0.5325,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 77:   8%| | 78/999 [01:00<13:11, 
[Running Accuracy]: 0.5256,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 78:   8%| | 78/999 [01:00<13:11,
 {'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 color of the second image more vibrant 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 focusing issues in 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 there focusing issues in both images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are there focusing issues in both images?\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([-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]: 0.5256,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 78:   8%| | 79/999 [01:01<13:26,
[Running Accuracy]: 0.5316,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 79:   8%| | 79/999 [01:01<13:26,
 {'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 there focusing issues in both 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>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([-31.0469], 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([-30.7344], 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]: 0.5316,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 79:   8%| | 80/999 [01:02<13:55,
[Running Accuracy]: 0.5375,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 80:   8%| | 80/999 [01:02<13:55, 
 {'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 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 second image, how is the color richness of the first image?
A. More color-rich
B. About the same
C. Less color-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 is the color richness of the first image?
A. More color-rich
B. About the same
C. Less color-rich
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the color richness of the first image?\nA. More color-rich\nB. About the same\nC. Less color-rich\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])
 C.

[Running Accuracy]: 0.5375,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 80:   8%| | 81/999 [01:03<13:27, 
[Running Accuracy]: 0.5432,[Response]: C.<|endoftext|>, [Correct Ans]: Less color-rich, , [Prog]: 81:   8%| | 81/999 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 color richness of the first image?\nA. More color-rich\nB. About the same\nC. Less color-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 second image, how is the lighting of the first image?
A. Slightly weak
B. Slightly strong
C. Almost 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 lighting of the first image?
A. Slightly weak
B. Slightly strong
C. Almost the same
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. Slightly weak\nB. Slightly strong\nC. Almost 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([-30.4844], 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]: 0.5432,[Response]: C.<|endoftext|>, [Correct Ans]: Less color-rich, , [Prog]: 81:   8%| | 82/999 [
[Running Accuracy]: 0.5488,[Response]: A.<|endoftext|>, [Correct Ans]: Slightly weak, , [Prog]: 82:   8%| | 82/999 [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 of the first image?\nA. Slightly weak\nB. Slightly strong\nC. Almost 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 does the realism of the first image compare?
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 second image, how does the realism of the first image compare?
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 second image, how does the realism of the first image compare?\nA. More realistic\nB. About the same\nC. Less realistic\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.7656], 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])
 C.

[Running Accuracy]: 0.5488,[Response]: A.<|endoftext|>, [Correct Ans]: Slightly weak, , [Prog]: 82:   8%| | 83/999 [01
[Running Accuracy]: 0.5422,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 83:   8%| | 83/999 [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 realism of the first image compare?\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 both images very authentic?
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 authentic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both images very authentic?\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([-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]: 0.5422,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 83:   8%| | 84/999 [0
[Running Accuracy]: 0.5476,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 84:   8%| | 84/999 [01:06<13:14, 
 {'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 very authentic?\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 of the first image stronger 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 stronger 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 stronger than the second image?\nA. Yes\nB. No\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])
 B.

[Running Accuracy]: 0.5476,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 84:   9%| | 85/999 [01:07<13:24, 
[Running Accuracy]: 0.5529,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 85:   9%| | 85/999 [01:07<13:24, 
 {'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 lighting of the first image stronger 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 in the two images is more affected by the loss of focus?
A. The building in the first image
B. The background 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 the loss of focus?
A. The building in the first image
B. The background 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 the loss of focus?\nA. The building in the first image\nB. The background 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([-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([-31.3281], 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]: 0.5529,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 85:   9%| | 86/999 [01:07<12:10, 
[Running Accuracy]: 0.5465,[Response]: B.<|endoftext|>, [Correct Ans]: The building in the first 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 area in the two images is more affected by the loss of focus?\nA. The building in the first image\nB. The background 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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. 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 of the following distortions does not appear in the two images?
A. Noise
B. Motion blur
C. Overexposure
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. Noise\nB. Motion blur\nC. Overexposure\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([-31.3750], 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]: 0.5465,[Response]: B.<|endoftext|>, [Correct Ans]: The building in the first image, , [Prog]: 86: 
[Running Accuracy]: 0.5517,[Response]: C.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 87:   9%| | 87/999 [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 distortions does not appear in the 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>Compared to the second image, how is the lighting of 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:Compared to the second image, how is the lighting of the first image?
A. similar
B. much worse
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. similar\nB. much worse\nC. much better\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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 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]: C.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 87:   9%| | 88/999 [01:
[Running Accuracy]: 0.5455,[Response]: C.<|endoftext|>, [Correct Ans]: similar, , [Prog]: 88:   9%| | 88/999 [01:09<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>Compared to the second image, how is the lighting of 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': '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 motion blur?
A. The horse in the second image
B. The floor in the first image
C. The pillar in the first image
D. The railing 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 horse in the second image
B. The floor in the first image
C. The pillar in the first image
D. The railing 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 horse in the second image\nB. The floor in the first image\nC. The pillar in the first image\nD. The railing 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([-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]: 0.5455,[Response]: C.<|endoftext|>, [Correct Ans]: similar, , [Prog]: 88:   9%| | 89/999 [01:10<12
[Running Accuracy]: 0.5393,[Response]: A.<|endoftext|>, [Correct Ans]: The pillar in the first image, , [Prog]: 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 area is more affected by motion blur?\nA. The horse in the second image\nB. The floor in the first image\nC. The pillar in the first image\nD. The railing 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 distortions is not present in the two images?
A. Motion blur
B. Blur
C. Out of focus
D. Overexposure
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 is not present in the two images?
A. Motion blur
B. Blur
C. Out of focus
D. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following distortions is not present in the two images?\nA. Motion blur\nB. Blur\nC. Out of focus\nD. Overexposure\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([-30.9062], 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]: 0.5393,[Response]: A.<|endoftext|>, [Correct Ans]: The pillar in the first image, , [Prog]: 89:   
[Running Accuracy]: 0.5444,[Response]: D.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 90:   9%| | 90/999 [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 distortions is not present in the two images?\nA. Motion blur\nB. Blur\nC. Out of focus\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 second image, is the first 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:Compared to the second image, is the first image more affected by motion blur?
A. Yes
B. No
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. 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([-30.6719], 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]: 0.5444,[Response]: D.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 90:   9%| | 91/999 [01:
[Running Accuracy]: 0.5495,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 91:   9%| | 91/999 [01:12<14:01,
 {'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, is the first 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>Compared to the second image, is the first 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:Compared to the second image, is the first image more affected by motion blur?
A. Yes
B. No
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. 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([-30.9531], 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]: 0.5495,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 91:   9%| | 92/999 [01:13<15:13,
[Running Accuracy]: 0.5435,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 92:   9%| | 92/999 [01:13<15:13, 
 {'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, is the first 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>Which of the following distortions is not present in the two images?
A. Underexposure
B. Low illumination
C. Motion blur
D. 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 is not present in the two images?
A. Underexposure
B. Low illumination
C. Motion blur
D. Blur
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following distortions is not present in the two images?\nA. Underexposure\nB. Low illumination\nC. Motion blur\nD. Blur\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([-30.7812], 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]: 0.5435,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 92:   9%| | 93/999 [01:14<14:42, 
[Running Accuracy]: 0.5376,[Response]: A.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 93:   9%| | 93/999 [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 of the following distortions is not present in the two images?\nA. Underexposure\nB. Low illumination\nC. Motion blur\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>Which one of the following distortions does not appear in the two images?
A. Underexposure
B. Blur
C. Noise
D. Vignetting
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Which one of the following distortions does not appear in the two images?
A. Underexposure
B. Blur
C. Noise
D. Vignetting
Answer with the option's letter from the given choices directly.

prompts: [["Which one of the following distortions does not appear in the two images?\nA. Underexposure\nB. Blur\nC. Noise\nD. Vignetting\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([-30.5938], 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]: 0.5376,[Response]: A.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 93:   9%| | 94/999 [01:1
[Running Accuracy]: 0.5426,[Response]: A.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 94:   9%| | 94/999 [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 one of the following distortions does not appear in the two images?\nA. Underexposure\nB. Blur\nC. Noise\nD. Vignetting\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 is not present in the two images?
A. Low light
B. Overexposure
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:Which of the following distortions is not present in the two images?
A. Low light
B. Overexposure
C. Motion blur
D. Underexposure
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following distortions is not present in the two images?\nA. Low light\nB. Overexposure\nC. Motion blur\nD. Underexposure\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([-30.7812], 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])
 D.

[Running Accuracy]: 0.5426,[Response]: A.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 94:  10%| | 95/999 [01
[Running Accuracy]: 0.5368,[Response]: D.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 95:  10%| | 95/999 [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 distortions is not present in the two images?\nA. Low light\nB. Overexposure\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>How does the focus of the first image compare to the second 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:How does the focus of the first image compare to the second image?
A. About the same
B. Worse
C. Better
Answer with the option's letter from the given choices directly.

prompts: [["How does the focus of the first image compare to the second image?\nA. About the same\nB. Worse\nC. Better\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])
 B.

[Running Accuracy]: 0.5368,[Response]: D.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 95:  10%| | 96/999 [01:
[Running Accuracy]: 0.5417,[Response]: B.<|endoftext|>, [Correct Ans]: Worse, , [Prog]: 96:  10%| | 96/999 [01:17<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>How does the focus of the first image compare to the second image?\nA. About the same\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>Compared to the second image, how is the lighting in the first image?
A. Worse
B. About the same
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. Worse
B. About the same
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. Worse\nB. About the same\nC. Better\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])
 C.

[Running Accuracy]: 0.5417,[Response]: B.<|endoftext|>, [Correct Ans]: Worse, , [Prog]: 96:  10%| | 97/999 [01:18<14:2
[Running Accuracy]: 0.5361,[Response]: C.<|endoftext|>, [Correct Ans]: Worse, , [Prog]: 97:  10%| | 97/999 [01:18<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 second image, how is the lighting in the first image?\nA. Worse\nB. About the same\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 lighting of 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 of 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 of 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([-30.4844], 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]: 0.5361,[Response]: C.<|endoftext|>, [Correct Ans]: Worse, , [Prog]: 97:  10%| | 98/999 [01:18<13:3
[Running Accuracy]: 0.5408,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 98:  10%| | 98/999 [01:18<13:37,
 {'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 lighting of 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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([-30.4375], 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([-30.2812], 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]: 0.5408,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 98:  10%| | 99/999 [01:19<12:28,
[Running Accuracy]: 0.5455,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 99:  10%| | 99/999 [01:19<12:28, 
 {'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 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>What issues are not present in the two images?
A. Blur
B. Noise
C. Overexposure
D. Low Clarity
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What issues are not present in the two images?
A. Blur
B. Noise
C. Overexposure
D. Low Clarity
Answer with the option's letter from the given choices directly.

prompts: [["What issues are not present in the two images?\nA. Blur\nB. Noise\nC. Overexposure\nD. Low Clarity\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([-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])
 C.

[Running Accuracy]: 0.5455,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 99:  10%| | 100/999 [01:20<12:45,
[Running Accuracy]: 0.5500,[Response]: C.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 100:  10%| | 100/999 [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 issues are not present in the two images?\nA. Blur\nB. Noise\nC. Overexposure\nD. Low Clarity\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 second image more vivid than the first?
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 more vivid than the first?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the second image more vivid than the first?\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([-31.0156], 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]: 0.5500,[Response]: C.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 100:  10%| | 101/999 [0
[Running Accuracy]: 0.5545,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 101:  10%| | 101/999 [01:21<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>Is the color of the second image more vivid than the first?\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 issues is not present in the two images?
A. Out of focus
B. Overexposure
C. Blur
D. Lens flare
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. Out of focus
B. Overexposure
C. Blur
D. Lens flare
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. Out of focus\nB. Overexposure\nC. Blur\nD. Lens flare\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([-31.2500], 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]: 0.5545,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 101:  10%| | 102/999 [01:22<11:5
[Running Accuracy]: 0.5588,[Response]: B.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 102:  10%| | 102/999 [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 issues is not present in the two images?\nA. Out of focus\nB. Overexposure\nC. Blur\nD. Lens flare\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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. 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?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first 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([-30.9844], 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]: 0.5588,[Response]: B.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 102:  10%| | 103/999 [0
[Running Accuracy]: 0.5534,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 103:  10%| | 103/999 [01:23<12:2
 {'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. 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 illuminations of the two images both 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 illuminations of the two images both good?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the illuminations of the two images both good?\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([-31.1719], 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]: 0.5534,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 103:  10%| | 104/999 [01:24<13:3
[Running Accuracy]: 0.5481,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 104:  10%| | 104/999 [01:24<13:37
 {'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 the illuminations of the two images both 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>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([-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([-31.2969], 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]: 0.5481,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 104:  11%| | 105/999 [01:24<12:10
[Running Accuracy]: 0.5429,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 105:  11%| | 105/999 [01:24<12:1
 {'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?\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 composition of the second image better?
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 second image better?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the composition of the second image better?\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([-31.2188], 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]: 0.5429,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 105:  11%| | 106/999 [01:25<11:2
[Running Accuracy]: 0.5377,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 106:  11%| | 106/999 [01:25<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 composition of the second image better?\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. Similar
B. Slightly higher
C. Slightly lower
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. Slightly higher
C. Slightly lower
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. Slightly higher\nC. Slightly lower\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]: 0.5377,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 106:  11%| | 107/999 [01:26<12:5
[Running Accuracy]: 0.5327,[Response]: C.<|endoftext|>, [Correct Ans]: Slightly higher, , [Prog]: 107:  11%| | 107/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. Slightly higher\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 focus 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 focus 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 focus 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([-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])
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]: 0.5327,[Response]: C.<|endoftext|>, [Correct Ans]: Slightly higher, , [Prog]: 107:  11%| | 108/999
[Running Accuracy]: 0.5370,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 108:  11%| | 108/999 [01:27<12:48
 {'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 focus 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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([-30.8750], 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([-31.5781], 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]: 0.5370,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 108:  11%| | 109/999 [01:28<12:56
[Running Accuracy]: 0.5413,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 109:  11%| | 109/999 [01:28<12:56
 {'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 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>Is the color of the first image richer 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 richer 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 richer 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([-31.1875], 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([-31.3906], 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]: 0.5413,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 109:  11%| | 110/999 [01:29<14:06
[Running Accuracy]: 0.5364,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 110:  11%| | 110/999 [01:29<14:06
 {'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>Is the color of the first image richer 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>Which type of distortion is not present in the two images?
A. Blur
B. Underexposure
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 type of distortion is not present in the two images?
A. Blur
B. Underexposure
C. Overexposure
D. Noise
Answer with the option's letter from the given choices directly.

prompts: [["Which type of distortion is not present in the two images?\nA. Blur\nB. Underexposure\nC. Overexposure\nD. Noise\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([-30.6250], 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]: 0.5364,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 110:  11%| | 111/999 [01:30<14:02
[Running Accuracy]: 0.5405,[Response]: B.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 111:  11%| | 111/999 [
 {'prompt': "A chat between a curious user and an artificial 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 not present in the two images?\nA. Blur\nB. Underexposure\nC. Overexposure\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>Compared to the second image, how would you rate the authenticity of the first image?
A. Higher
B. About the same
C. 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 would you rate the authenticity of the first image?
A. Higher
B. About the same
C. Lower
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. Higher\nB. About the same\nC. Lower\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])
 C.

[Running Accuracy]: 0.5405,[Response]: B.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 111:  11%| | 112/999 [
[Running Accuracy]: 0.5357,[Response]: C.<|endoftext|>, [Correct Ans]: Higher, , [Prog]: 112:  11%| | 112/999 [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 second image, how would you rate the authenticity of the first image?\nA. Higher\nB. About the same\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>Compared to the second image, how is the focus of the first image?
A. Worse
B. About the same
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 focus of the first image?
A. Worse
B. About the same
C. Better
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the focus of the first image?\nA. Worse\nB. About the same\nC. Better\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([-30.9062], 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]: 0.5357,[Response]: C.<|endoftext|>, [Correct Ans]: Higher, , [Prog]: 112:  11%| | 113/999 [01:32<1
[Running Accuracy]: 0.5398,[Response]: A.<|endoftext|>, [Correct Ans]: Worse, , [Prog]: 113:  11%| | 113/999 [01:32<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 focus of the first image?\nA. Worse\nB. About the same\nC. 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 distortions did not appear in the two images?
A. Motion blur
B. Overexposure
C. Blur
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. Motion blur
B. Overexposure
C. Blur
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. Motion blur\nB. Overexposure\nC. Blur\nD. Underexposure\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([-30.3594], 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])
 D.

[Running Accuracy]: 0.5398,[Response]: A.<|endoftext|>, [Correct Ans]: Worse, , [Prog]: 113:  11%| | 114/999 [01:33<13
[Running Accuracy]: 0.5439,[Response]: D.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 114:  11%| | 114/999 [
 {'prompt': "A chat between a curious user and an artificial 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. Motion blur\nB. Overexposure\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>Which area is more affected by blurring?
A. The bushes in the first image
B. The background in the second image
C. The bird in the second image
D. The mirror reflection 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 bushes in the first image
B. The background in the second image
C. The bird in the second image
D. The mirror reflection 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 bushes in the first image\nB. The background in the second image\nC. The bird in the second image\nD. The mirror reflection 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([-31.3906], 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]: 0.5439,[Response]: D.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 114:  12%| | 115/999 [
[Running Accuracy]: 0.5478,[Response]: B.<|endoftext|>, [Correct Ans]: The background in the second image, , [Prog]: 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 more affected by blurring?\nA. The bushes in the first image\nB. The background in the second image\nC. The bird in the second image\nD. The mirror reflection 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 area in the two images is more affected by underexposure?
A. The phone in the second image
B. The wall in the second image
C. The trees in the first image
D. 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 area in the two images is more affected by underexposure?
A. The phone in the second image
B. The wall in the second image
C. The trees in the first image
D. The person in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area in the two images is more affected by underexposure?\nA. The phone in the second image\nB. The wall in the second image\nC. The trees in the first image\nD. The person 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([-31.1875], 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]: 0.5478,[Response]: B.<|endoftext|>, [Correct Ans]: The background in the second image, , [Prog]: 1
[Running Accuracy]: 0.5431,[Response]: A.<|endoftext|>, [Correct Ans]: The trees in the first image, , [Prog]: 116:  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 in the two images is more affected by underexposure?\nA. The phone in the second image\nB. The wall in the second image\nC. The trees in the first image\nD. 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 second image, how is the sharpness 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 sharpness 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 sharpness 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([-30.3750], 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])
 A.

[Running Accuracy]: 0.5431,[Response]: A.<|endoftext|>, [Correct Ans]: The trees in the first image, , [Prog]: 116:  1
[Running Accuracy]: 0.5385,[Response]: A.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 117:  12%| | 117/999 [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 second image, how is the sharpness 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>Are both images adequately illuminated?
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 adequately illuminated?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both images adequately illuminated?\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([-31.0469], 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]: 0.5385,[Response]: A.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 117:  12%| | 118/999 [01:37<1
[Running Accuracy]: 0.5424,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 118:  12%| | 118/999 [01:37<15:21
 {'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 adequately illuminated?\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 are the problems that did not appear in the two images?
A. underexposure
B. blur
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:What are the problems that did not appear in the two images?
A. underexposure
B. blur
C. motion blur
D. noise
Answer with the option's letter from the given choices directly.

prompts: [["What are the problems that did not appear in the two images?\nA. underexposure\nB. blur\nC. motion blur\nD. noise\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([-30.2500], 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]: 0.5424,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 118:  12%| | 119/999 [01:37<13:24
[Running Accuracy]: 0.5378,[Response]: A.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 119:  12%| | 119/999 [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 are the problems that did not appear in the two images?\nA. underexposure\nB. blur\nC. motion blur\nD. 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 second image, how is the clarity of the first image?
A. similar
B. slightly low
C. slightly high
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. similar
B. slightly low
C. slightly high
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. similar\nB. slightly low\nC. slightly high\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])
 B.

[Running Accuracy]: 0.5378,[Response]: A.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 119:  12%| | 120/999 [01
[Running Accuracy]: 0.5333,[Response]: B.<|endoftext|>, [Correct Ans]: slightly high, , [Prog]: 120:  12%| | 120/999 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. similar\nB. slightly low\nC. slightly high\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 issues are not present in the two images?
A. Blur
B. Underexposure
C. Out of focus
D. Overexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What issues are not present in the two images?
A. Blur
B. Underexposure
C. Out of focus
D. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["What issues are not present in the two images?\nA. Blur\nB. Underexposure\nC. Out of focus\nD. Overexposure\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])
 D.

[Running Accuracy]: 0.5333,[Response]: B.<|endoftext|>, [Correct Ans]: slightly high, , [Prog]: 120:  12%| | 121/999 [
[Running Accuracy]: 0.5289,[Response]: D.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 121:  12%| | 121/999 [
 {'prompt': "A chat between a curious user and an artificial 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 issues are not present in the two images?\nA. Blur\nB. Underexposure\nC. Out of focus\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 content of the first image more complete 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 content of the first image more complete than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the content of the first image more complete than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.1250], 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([-30.9688], 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. No

[Running Accuracy]: 0.5289,[Response]: D.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 121:  12%| | 122/999 [
[Running Accuracy]: 0.5246,[Response]: A. No<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 122:  12%| | 122/999 [01:39<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 content of the first image more complete than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A. No<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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 focus of 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:Compared to the second image, how is the focus of the first image?
A. worse
B. better
C. similar
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the focus of the first image?\nA. worse\nB. better\nC. similar\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([-30.6562], 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]: 0.5246,[Response]: A. No<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 122:  12%| | 123/999 [01:40<1
[Running Accuracy]: 0.5285,[Response]: A.<|endoftext|>, [Correct Ans]: worse, , [Prog]: 123:  12%| | 123/999 [01:40<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 focus of the first image?\nA. worse\nB. better\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>Are the details and textures of the two 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 the details and textures of the two images very clear?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the details and textures of the two images very clear?\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([-31.1562], 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]: 0.5285,[Response]: A.<|endoftext|>, [Correct Ans]: worse, , [Prog]: 123:  12%| | 124/999 [01:41<11
[Running Accuracy]: 0.5323,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 124:  12%| | 124/999 [01:41<11:19
 {'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 the details and textures of the two 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>What additional distortion does the second image have compared to the first image?
A. underexposure
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:What additional distortion does the second image have compared to the first image?
A. underexposure
B. motion blur
C. overexposure
Answer with the option's letter from the given choices directly.

prompts: [["What additional distortion does the second image have compared to the first image?\nA. underexposure\nB. motion blur\nC. overexposure\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])
 C.

[Running Accuracy]: 0.5323,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 124:  13%|▏| 125/999 [01:42<10:34
[Running Accuracy]: 0.5360,[Response]: C.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 125:  13%|▏| 125/999 [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 additional distortion does the second image have compared to the first image?\nA. underexposure\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>Comparing to the first image, how is the detail texture of the object in 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:Comparing to the first image, how is the detail texture of the object in the second image?
A. More blurry
B. Clearer
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Comparing to the first image, how is the detail texture of the object in 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([-30.9375], 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([-31.4844], 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]: 0.5360,[Response]: C.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 125:  13%|▏| 126/999 [0
[Running Accuracy]: 0.5397,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 126:  13%|▏| 126/999 [01:42<
 {'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>Comparing to the first image, how is the detail texture of the object in 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 texture of the object in 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 texture of the object in 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 texture of the object in the second image?\nA. Similar\nB. Clearer\nC. Blurrier\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])
 B.

[Running Accuracy]: 0.5397,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 126:  13%|▏| 127/999 [01:43<
[Running Accuracy]: 0.5433,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 127:  13%|▏| 127/999 [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>Compared to the first image, how is the texture of the object in 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>Compared to the second image, how is the degree of overexposure in the first image?
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 second image, how is the degree of overexposure in the first image?
A. smaller
B. about the same
C. larger
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the degree of overexposure in the first image?\nA. smaller\nB. about the same\nC. larger\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([-31.2969], 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]: 0.5433,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 127:  13%|▏| 128/999 [01:44<
[Running Accuracy]: 0.5469,[Response]: C.<|endoftext|>, [Correct Ans]: larger, , [Prog]: 128:  13%|▏| 128/999 [01:44<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 degree of overexposure in the first image?\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. 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?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the first image more realistic?\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([-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]: 0.5469,[Response]: C.<|endoftext|>, [Correct Ans]: larger, , [Prog]: 128:  13%|▏| 129/999 [01:45<1
[Running Accuracy]: 0.5426,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 129:  13%|▏| 129/999 [01:45<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 first 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 images lit normally?
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 lit normally?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both images lit normally?\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([-30.8906], 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]: 0.5426,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 129:  13%|▏| 130/999 [01:45<09:5
[Running Accuracy]: 0.5385,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 130:  13%|▏| 130/999 [01:45<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>Are both images lit normally?\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 color distortions in 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 there color distortions in both images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are there color distortions in both 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([-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]: 0.5385,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 130:  13%|▏| 131/999 [01:46<09:3
[Running Accuracy]: 0.5420,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 131:  13%|▏| 131/999 [01:46<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>Are there color distortions in both 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>What kind of distortion does the first image have that the second image does not?
A. Underexposure
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:What kind of distortion does the first image have that the second image does not?
A. Underexposure
B. Overexposure
C. Motion blur
D. Noise
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion does the first image have that the second image does not?\nA. Underexposure\nB. Overexposure\nC. Motion blur\nD. Noise\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([-30.9219], 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]: 0.5420,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 131:  13%|▏| 132/999 [01:47<11:4
[Running Accuracy]: 0.5379,[Response]: C.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 132:  13%|▏| 132/999 [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 have that the second image does not?\nA. Underexposure\nB. Overexposure\nC. Motion blur\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 noise situation in the second image?
A. More serious
B. About the same
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. More serious
B. About the same
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. More serious\nB. About the same\nC. Slighter\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])
 A.

[Running Accuracy]: 0.5379,[Response]: C.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 132:  13%|▏| 133/999 [0
[Running Accuracy]: 0.5414,[Response]: A.<|endoftext|>, [Correct Ans]: More serious, , [Prog]: 133:  13%|▏| 133/999 [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. More serious\nB. About the same\nC. 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>Which area is more affected by blur?
A. Background in the second image
B. Man in the first image
C. Girl in the first image
D. Woman 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. Background in the second image
B. Man in the first image
C. Girl in the first image
D. Woman in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more affected by blur?\nA. Background in the second image\nB. Man in the first image\nC. Girl in the first image\nD. Woman 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([-30.6875], 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]: 0.5414,[Response]: A.<|endoftext|>, [Correct Ans]: More serious, , [Prog]: 133:  13%|▏| 134/999 [0
[Running Accuracy]: 0.5448,[Response]: A.<|endoftext|>, [Correct Ans]: Background in the second image, , [Prog]: 134: 
 {'prompt': "A chat between a curious user and an artificial 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. Background in the second image\nB. Man in the first image\nC. Girl in the first image\nD. Woman 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 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([-31.2969], 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([-31.0781], 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]: 0.5448,[Response]: A.<|endoftext|>, [Correct Ans]: Background in the second image, , [Prog]: 134: 
[Running Accuracy]: 0.5481,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 135:  14%|▏| 135/999 [01:49<10:18
 {'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 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([-31.4531], 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]: 0.5481,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 135:  14%|▏| 136/999 [01:50<09:49
[Running Accuracy]: 0.5515,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 136:  14%|▏| 136/999 [01:50<09:49
 {'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>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, how is the focus of the second 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:Compared to the first image, how is the focus of the second image?
A. Similar
B. Much worse
C. Much better
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. Much worse\nC. Much better\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])
 B.

[Running Accuracy]: 0.5515,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 136:  14%|▏| 137/999 [01:51<11:08
[Running Accuracy]: 0.5474,[Response]: B.<|endoftext|>, [Correct Ans]: Much better, , [Prog]: 137:  14%|▏| 137/999 [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 focus of the second 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>Which type of distortion is not more present in the second image than in the first image?
A. underexposure
B. ghosting
C. lens flare
D. overexposure
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 not more present in the second image than in the first image?
A. underexposure
B. ghosting
C. lens flare
D. overexposure
Answer with the option's letter from the given choices directly.

prompts: [["Which type of distortion is not more present in the second image than in the first image?\nA. underexposure\nB. ghosting\nC. lens flare\nD. overexposure\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([-31.2031], 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]: 0.5474,[Response]: B.<|endoftext|>, [Correct Ans]: Much better, , [Prog]: 137:  14%|▏| 138/999 [01
[Running Accuracy]: 0.5435,[Response]: A.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 138:  14%|▏| 138/999 [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 not more present in the second image than in the first image?\nA. underexposure\nB. ghosting\nC. lens flare\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>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([-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([-31.4688], 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]: 0.5435,[Response]: A.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 138:  14%|▏| 139/999 [0
[Running Accuracy]: 0.5396,[Response]: C.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 139:  14%|▏| 139/999 [01:52<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 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>Are both images 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:Are both images affected by motion blur?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both images affected by motion blur?\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([-30.9844], 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]: 0.5396,[Response]: C.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 139:  14%|▏| 140/999 [01:53<1
[Running Accuracy]: 0.5357,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 140:  14%|▏| 140/999 [01:53<10:43
 {'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 both images 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>Are there serious focusing issues 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 serious focusing issues in both images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are there serious focusing issues in both images?\nA. No\nB. Yes\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([-31.1719], 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]: 0.5357,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 140:  14%|▏| 141/999 [01:53<10:04
[Running Accuracy]: 0.5319,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 141:  14%|▏| 141/999 [01:53<10:04
 {'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 there serious focusing issues 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>Are both images 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:Are both images affected by motion blur?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both images 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([-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]: 0.5319,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 141:  14%|▏| 142/999 [01:54<10:51
[Running Accuracy]: 0.5352,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 142:  14%|▏| 142/999 [01:54<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>Are both images 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>Compared to the first image, how much is the second image affected by motion blur?
A. About the same
B. Bigger
C. Smaller
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. About the same
B. Bigger
C. Smaller
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. About the same\nB. Bigger\nC. Smaller\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([-31.3125], 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]: 0.5352,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 142:  14%|▏| 143/999 [01:55<10:1
[Running Accuracy]: 0.5385,[Response]: B.<|endoftext|>, [Correct Ans]: Bigger, , [Prog]: 143:  14%|▏| 143/999 [01:55<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 much is the second image affected by motion blur?\nA. About the same\nB. Bigger\nC. Smaller\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 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 images overexposed?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both images overexposed?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
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])
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]: 0.5385,[Response]: B.<|endoftext|>, [Correct Ans]: Bigger, , [Prog]: 143:  14%|▏| 144/999 [01:55<0
[Running Accuracy]: 0.5417,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 144:  14%|▏| 144/999 [01:55<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>Are both 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. Much clearer
B. Much 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. Much clearer
B. Much 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. Much clearer\nB. Much blurrier\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([-31.2031], 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]: 0.5417,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 144:  15%|▏| 145/999 [01:56<09:33
[Running Accuracy]: 0.5448,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 145:  15%|▏| 145/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 clearer\nB. Much 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>Which type of distortion is not present in the two images?
A. Overexposure
B. Blur
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 type of distortion is not present in the two images?
A. Overexposure
B. Blur
C. Out of focus
D. Motion blur
Answer with the option's letter from the given choices directly.

prompts: [["Which type of distortion is not present in the two images?\nA. Overexposure\nB. Blur\nC. Out of focus\nD. Motion blur\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]: 0.5448,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 145:  15%|▏| 146/999 
[Running Accuracy]: 0.5411,[Response]: A.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 146:  15%|▏| 146/999 [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 type of distortion is not present in the two images?\nA. Overexposure\nB. Blur\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>Is the lighting weaker 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 lighting weaker in the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the lighting weaker in 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([-30.9531], 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]: 0.5411,[Response]: A.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 146:  15%|▏| 147/999 [01
[Running Accuracy]: 0.5442,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 147:  15%|▏| 147/999 [01:57<09:1
 {'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 lighting weaker 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 does the lighting of the second image compare to the first image?
A. similar
B. slightly stronger
C. slightly weaker
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. similar
B. slightly stronger
C. slightly weaker
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. similar\nB. slightly stronger\nC. slightly weaker\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])
 C.

[Running Accuracy]: 0.5442,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 147:  15%|▏| 148/999 [01:58<09:1
[Running Accuracy]: 0.5405,[Response]: C.<|endoftext|>, [Correct Ans]: slightly stronger, , [Prog]: 148:  15%|▏| 148/9
 {'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>How does the lighting of the second image compare to the first image?\nA. similar\nB. slightly stronger\nC. slightly 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 is the focus of the second image?
A. Worse
B. About the same
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 first image, how is the focus of the second image?
A. Worse
B. About the same
C. Better
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. Worse\nB. About the same\nC. Better\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([-31.6094], 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])
 A.

[Running Accuracy]: 0.5405,[Response]: C.<|endoftext|>, [Correct Ans]: slightly stronger, , [Prog]: 148:  15%|▏| 149/9
[Running Accuracy]: 0.5369,[Response]: A.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 149:  15%|▏| 149/999 [01:59<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 focus of the second image?\nA. Worse\nB. About the same\nC. 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 area is more affected by low light?
A. the building in the first image
B. LED in the first image
C. the snowy area in the second image
D. the penguin 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 low light?
A. the building in the first image
B. LED in the first image
C. the snowy area in the second image
D. the penguin in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more affected by low light?\nA. the building in the first image\nB. LED in the first image\nC. the snowy area in the second image\nD. the penguin 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([-31.0781], 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]: 0.5369,[Response]: A.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 149:  15%|▏| 150/999 [01:59<0
[Running Accuracy]: 0.5400,[Response]: A.<|endoftext|>, [Correct Ans]: the building in the first image, , [Prog]: 150:
 {'prompt': "A chat between a curious user and an artificial 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 building in the first image\nB. LED in the first image\nC. the snowy area in the second image\nD. the penguin 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 second 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:Is the second image more affected by motion blur?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the second image more affected by motion blur?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.8281], 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([-30.9531], 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]: 0.5400,[Response]: A.<|endoftext|>, [Correct Ans]: the building in the first image, , [Prog]: 150:
[Running Accuracy]: 0.5430,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 151:  15%|▏| 151/999 [02:00<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>Is the second 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>Compared to the second image, how is the lighting in the first image?
A. Similar
B. Much weaker
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 weaker
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 weaker\nC. Much stronger\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([-31.0156], 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]: 0.5430,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 151:  15%|▏| 152/999 [02:01<09:2
[Running Accuracy]: 0.5461,[Response]: B.<|endoftext|>, [Correct Ans]: Much weaker, , [Prog]: 152:  15%|▏| 152/999 [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 lighting in the first image?\nA. Similar\nB. Much weaker\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 lighting in the second image?
A. Stronger
B. About the same
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. Stronger
B. About the same
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. Stronger\nB. About the same\nC. Weaker\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]: 0.5461,[Response]: B.<|endoftext|>, [Correct Ans]: Much weaker, , [Prog]: 152:  15%|▏| 153/999 [02
[Running Accuracy]: 0.5490,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 153:  15%|▏| 153/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. Stronger\nB. About the same\nC. Weaker\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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 details and textures in 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 clarity of the details and textures in 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 clarity of the details and textures in the second image?\nA. More blurry\nB. Clearer\nC. Similar\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([-31.4844], 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]: 0.5490,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 153:  15%|▏| 154/999 
[Running Accuracy]: 0.5455,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 154:  15%|▏| 154/999 [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 details and textures in the second image?\nA. More blurry\nB. Clearer\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>The first image has many more issues compared to the second image, which of the following is not included in these issues?
A. Motion blur
B. Underexposure
C. Poor composition
D. Out of focus
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:The first image has many more issues compared to the second image, which of the following is not included in these issues?
A. Motion blur
B. Underexposure
C. Poor composition
D. Out of focus
Answer with the option's letter from the given choices directly.

prompts: [["The first image has many more issues compared to the second image, which of the following is not included in these issues?\nA. Motion blur\nB. Underexposure\nC. Poor composition\nD. Out of focus\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])
 B.

[Running Accuracy]: 0.5455,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 154:  16%|▏| 155/999 [02
[Running Accuracy]: 0.5419,[Response]: B.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 155:  16%|▏| 155/999 [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>The first image has many more issues compared to the second image, which of the following is not included in these issues?\nA. Motion blur\nB. Underexposure\nC. Poor composition\nD. 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 of the following issues did not appear in the two images?
A. Out of focus
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 of the following issues did not appear in the two images?
A. Out of focus
B. Motion blur
C. Overexposure
D. Blur
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following issues did not appear in the two images?\nA. Out of focus\nB. Motion blur\nC. Overexposure\nD. Blur\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([-30.9688], 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]: 0.5419,[Response]: B.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 155:  16%|▏| 156/999 [02
[Running Accuracy]: 0.5385,[Response]: C.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 156:  16%|▏| 156/999 [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>Which of the following issues did not appear in the two images?\nA. Out of focus\nB. Motion blur\nC. Overexposure\nD. 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>Is the color of 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 color of the first image more realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the first image more 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([-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]: 0.5385,[Response]: C.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 156:  16%|▏| 157/999 [02
[Running Accuracy]: 0.5350,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 157:  16%|▏| 157/999 [02:04<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>Is the color of 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 area is more affected by blurring?
A. The flowers in front of the lens in the first image
B. The upper half area in the second image
C. The human feet in the second image
D. The bushes 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 flowers in front of the lens in the first image
B. The upper half area in the second image
C. The human feet in the second image
D. The bushes 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 flowers in front of the lens in the first image\nB. The upper half area in the second image\nC. The human feet in the second image\nD. The bushes in the first 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([-31.2031], 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]: 0.5350,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 157:  16%|▏| 158/999 [02:05<09:2
[Running Accuracy]: 0.5380,[Response]: B.<|endoftext|>, [Correct Ans]: The upper half area 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 affected by blurring?\nA. The flowers in front of the lens in the first image\nB. The upper half area in the second image\nC. The human feet in the second image\nD. The bushes 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, does the first image have more underexposure 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:Compared to the second image, does the first image have more underexposure issues?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, does the first image have more underexposure issues?\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([-31.1562], 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]: 0.5380,[Response]: B.<|endoftext|>, [Correct Ans]: The upper half area in the second image, , [Pro
[Running Accuracy]: 0.5346,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 159:  16%|▏| 159/999 [02:05<09:08
 {'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 second image, does the first image have more underexposure issues?\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 much is the impact of overexposure on the second image?
A. Much weaker
B. Similar
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 first image, how much is the impact of overexposure on the second image?
A. Much weaker
B. Similar
C. Much stronger
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how much is the impact of overexposure on the second image?\nA. Much weaker\nB. Similar\nC. Much stronger\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([-31.0156], 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])
 C.

[Running Accuracy]: 0.5346,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 159:  16%|▏| 160/999 [02:06<09:11
[Running Accuracy]: 0.5375,[Response]: C.<|endoftext|>, [Correct Ans]: Much stronger, , [Prog]: 160:  16%|▏| 160/999 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 impact of overexposure on the second image?\nA. Much weaker\nB. Similar\nC. Much stronger\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 region in the two images is more affected by blurring?
A. The background crowd in the second image
B. The branches in the first image
C. The leaves in the first image
D. The man in front of the lens 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 region in the two images is more affected by blurring?
A. The background crowd in the second image
B. The branches in the first image
C. The leaves in the first image
D. The man in front of the lens in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which region in the two images is more affected by blurring?\nA. The background crowd in the second image\nB. The branches in the first image\nC. The leaves in the first image\nD. The man in front of the lens 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([-31.3906], 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]: 0.5375,[Response]: C.<|endoftext|>, [Correct Ans]: Much stronger, , [Prog]: 160:  16%|▏| 161/999 [
[Running Accuracy]: 0.5404,[Response]: A.<|endoftext|>, [Correct Ans]: The background crowd 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 region in the two images is more affected by blurring?\nA. The background crowd in the second image\nB. The branches in the first image\nC. The leaves in the first image\nD. The man in front of the lens 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 focus of the first image?
A. No difference
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 second image, how is the focus of the first image?
A. No difference
B. Better
C. Worse
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the focus of the first image?\nA. No difference\nB. Better\nC. 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([-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]: 0.5404,[Response]: A.<|endoftext|>, [Correct Ans]: The background crowd in the second image, , [Pr
[Running Accuracy]: 0.5370,[Response]: C.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 162:  16%|▏| 162/999 [02:07<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>Compared to the second image, how is the focus of the first image?\nA. No difference\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>Are the compositions of these two images both aesthetically pleasing?
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 compositions of these two images both aesthetically pleasing?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the compositions of these two images both aesthetically pleasing?\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([-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]: 0.5370,[Response]: C.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 162:  16%|▏| 163/999 [02:08<0
[Running Accuracy]: 0.5399,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 163:  16%|▏| 163/999 [02:08<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>Are the compositions of these two images both aesthetically pleasing?\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 lighting situation in the second 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 first image, how is the lighting situation in the second 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 first image, how is the lighting situation in the second 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([-31.2188], 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([-30.8594], 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]: 0.5399,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 163:  16%|▏| 164/999 [02:08<08:4
[Running Accuracy]: 0.5427,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 164:  16%|▏| 164/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. 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 second image, how much is the impact of overexposure on the first image?
A. Similar
B. Larger
C. Smaller
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 much is the impact of overexposure on the first image?
A. Similar
B. Larger
C. Smaller
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how much is the impact of overexposure on the first image?\nA. Similar\nB. Larger\nC. Smaller\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([-31.4375], 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]: 0.5427,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 164:  17%|▏| 165/999 
[Running Accuracy]: 0.5455,[Response]: B.<|endoftext|>, [Correct Ans]: Larger, , [Prog]: 165:  17%|▏| 165/999 [02:09<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 second image, how much is the impact of overexposure on the first image?\nA. Similar\nB. Larger\nC. Smaller\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 images shown underexposure issues?
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 images shown underexposure issues?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Have both images shown underexposure issues?\nA. No\nB. Yes\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([-31.0938], 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]: 0.5455,[Response]: B.<|endoftext|>, [Correct Ans]: Larger, , [Prog]: 165:  17%|▏| 166/999 [02:10<0
[Running Accuracy]: 0.5482,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 166:  17%|▏| 166/999 [02:10<09:05
 {'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>Have both images shown underexposure issues?\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 in the first image?
A. Stronger
B. Similar
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 second image, how is the lighting condition in the first image?
A. Stronger
B. Similar
C. Weaker
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the lighting condition in the first image?\nA. Stronger\nB. Similar\nC. Weaker\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([-30.8594], 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]: 0.5482,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 166:  17%|▏| 167/999 [02:10<09:02
[Running Accuracy]: 0.5449,[Response]: C.<|endoftext|>, [Correct Ans]: Stronger, , [Prog]: 167:  17%|▏| 167/999 [02:10
 {'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 lighting condition in the first image?\nA. Stronger\nB. Similar\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 is the richness of colors in the second image?
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 is the richness of colors in the second image?
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 is the richness of colors in the second image?\nA. Less rich\nB. More rich\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3594], 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([-30.9375], 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]: 0.5449,[Response]: C.<|endoftext|>, [Correct Ans]: Stronger, , [Prog]: 167:  17%|▏| 168/999 [02:11
[Running Accuracy]: 0.5476,[Response]: B.<|endoftext|>, [Correct Ans]: More rich, , [Prog]: 168:  17%|▏| 168/999 [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>Compared to the first image, how is the richness of colors in the second image?\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 second image, how is the lighting situation in the first image?
A. Similar
B. Weaker
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 second image, how is the lighting situation in the first image?
A. Similar
B. Weaker
C. Stronger
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. Weaker\nC. Stronger\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]: 0.5476,[Response]: B.<|endoftext|>, [Correct Ans]: More rich, , [Prog]: 168:  17%|▏| 169/999 [02:1
[Running Accuracy]: 0.5503,[Response]: B.<|endoftext|>, [Correct Ans]: Weaker, , [Prog]: 169:  17%|▏| 169/999 [02:12<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 second image, how is the lighting situation in the first image?\nA. Similar\nB. Weaker\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 composition of 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 composition of 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 composition of 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([-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([-30.8281], 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]: 0.5503,[Response]: B.<|endoftext|>, [Correct Ans]: Weaker, , [Prog]: 169:  17%|▏| 170/999 [02:13<1
[Running Accuracy]: 0.5529,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 170:  17%|▏| 170/999 [02:13<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 composition of 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>Compared to the first image, how much is the second image affected by blurring?
A. Similar
B. Smaller
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 much is the second image affected by blurring?
A. Similar
B. Smaller
C. Larger
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 blurring?\nA. Similar\nB. Smaller\nC. Larger\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([-29.8125], 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])
 C.

[Running Accuracy]: 0.5529,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 170:  17%|▏| 171/999 [02:14<10:2
[Running Accuracy]: 0.5556,[Response]: C.<|endoftext|>, [Correct Ans]: Larger, , [Prog]: 171:  17%|▏| 171/999 [02:14<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 much is the second image affected by blurring?\nA. Similar\nB. Smaller\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 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([-31.1562], 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([-31.1094], 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]: 0.5556,[Response]: C.<|endoftext|>, [Correct Ans]: Larger, , [Prog]: 171:  17%|▏| 172/999 [02:14<0
[Running Accuracy]: 0.5523,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 172:  17%|▏| 172/999 [02:14<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 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>What is the distortion that did not appear in the two images?
A. Motion blur
B. Low pixel
C. Underexposure
D. Distortion
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 did not appear in the two images?
A. Motion blur
B. Low pixel
C. Underexposure
D. Distortion
Answer with the option's letter from the given choices directly.

prompts: [["What is the distortion that did not appear in the two images?\nA. Motion blur\nB. Low pixel\nC. Underexposure\nD. Distortion\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([-31.0312], 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]: 0.5523,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 172:  17%|▏| 173/999 [02:15<09:3
[Running Accuracy]: 0.5491,[Response]: A.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 173:  17%|▏| 173/999 [
 {'prompt': "A chat between a curious user and an artificial 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 did not appear in the two images?\nA. Motion blur\nB. Low pixel\nC. Underexposure\nD. 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 second image affected by blurring?
A. Similar
B. Smaller
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 blurring?
A. Similar
B. Smaller
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 blurring?\nA. Similar\nB. Smaller\nC. Larger\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([-30.8750], 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]: 0.5491,[Response]: A.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 173:  17%|▏| 174/999 [
[Running Accuracy]: 0.5517,[Response]: B.<|endoftext|>, [Correct Ans]: Smaller, , [Prog]: 174:  17%|▏| 174/999 [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>Compared to the first image, how is the second image affected by blurring?\nA. Similar\nB. Smaller\nC. Larger\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 images have significant noise?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Did both images have significant noise?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Did both images have significant noise?\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([-30.7969], 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]: 0.5517,[Response]: B.<|endoftext|>, [Correct Ans]: Smaller, , [Prog]: 174:  18%|▏| 175/999 [02:16<
[Running Accuracy]: 0.5486,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 175:  18%|▏| 175/999 [02:16<08:40
 {'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>Did both images have significant noise?\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 distortion that does not appear in the two images?
A. underexposure
B. overexposure
C. low light
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 distortion that does not appear in the two images?
A. underexposure
B. overexposure
C. low light
D. motion blur
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. overexposure\nC. low light\nD. motion blur\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([-31.0938], 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]: 0.5486,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 175:  18%|▏| 176/999 [02:17<09:20
[Running Accuracy]: 0.5455,[Response]: A.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 176:  18%|▏| 176/999 [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 distortion that does not appear in the two images?\nA. underexposure\nB. overexposure\nC. low light\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 are the pixels of the second image?
A. Lower
B. Higher
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 are the pixels of the second image?
A. Lower
B. Higher
C. Similar
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how are the pixels of the second image?\nA. Lower\nB. Higher\nC. Similar\nAnswer with the option's letter from the given choices directly.\n"]]
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])
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]: 0.5455,[Response]: A.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 176:  18%|▏| 177/999 [02
[Running Accuracy]: 0.5480,[Response]: B.<|endoftext|>, [Correct Ans]: Higher, , [Prog]: 177:  18%|▏| 177/999 [02:17<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 are the pixels of the second image?\nA. Lower\nB. Higher\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 second image affected by blurring?
A. Similar
B. Slightly smaller
C. Much 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 blurring?
A. Similar
B. Slightly smaller
C. Much 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 blurring?\nA. Similar\nB. Slightly smaller\nC. Much larger\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]: 0.5480,[Response]: B.<|endoftext|>, [Correct Ans]: Higher, , [Prog]: 177:  18%|▏| 178/999 [02:18<0
[Running Accuracy]: 0.5449,[Response]: A.<|endoftext|>, [Correct Ans]: Slightly smaller, , [Prog]: 178:  18%|▏| 178/99
 {'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>Compared to the first image, how is the second image affected by blurring?\nA. Similar\nB. Slightly smaller\nC. Much 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>Is the content of the first image clearer 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 content of the first image clearer than that of the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the content of the first image clearer than that of the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.4688], 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([-30.7500], 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]: 0.5449,[Response]: A.<|endoftext|>, [Correct Ans]: Slightly smaller, , [Prog]: 178:  18%|▏| 179/99
[Running Accuracy]: 0.5419,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 179:  18%|▏| 179/999 [02:19<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>Is the content of the first image clearer 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>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([-31.2188], 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([-31.4062], 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]: 0.5419,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 179:  18%|▏| 180/999 [02:19<08:27
[Running Accuracy]: 0.5444,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 180:  18%|▏| 180/999 [02:19<08:27
 {'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 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>How does the richness of colors in the second image compare to the first 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:How does the richness of colors in the second image compare to the first image?
A. Much poorer
B. Much richer
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["How does the richness of colors in the second image compare to the first 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([-31.2812], 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])
 B.

[Running Accuracy]: 0.5444,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 180:  18%|▏| 181/999 [02:20<08:20
[Running Accuracy]: 0.5470,[Response]: B.<|endoftext|>, [Correct Ans]: Much richer, , [Prog]: 181:  18%|▏| 181/999 [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 richness of colors in the second image compare to the first 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>Compared to the first image, how are the pixels in the second image?
A. Higher
B. About the same
C. Lower
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 pixels in the second image?
A. Higher
B. About the same
C. Lower
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how are the pixels in the second image?\nA. Higher\nB. About the same\nC. Lower\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([-30.6719], 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]: 0.5470,[Response]: B.<|endoftext|>, [Correct Ans]: Much richer, , [Prog]: 181:  18%|▏| 182/999 [02
[Running Accuracy]: 0.5495,[Response]: C.<|endoftext|>, [Correct Ans]: Lower, , [Prog]: 182:  18%|▏| 182/999 [02:20<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 are the pixels in the second image?\nA. Higher\nB. About the same\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>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([-31.2344], 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([-30.6719], 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]: 0.5495,[Response]: C.<|endoftext|>, [Correct Ans]: Lower, , [Prog]: 182:  18%|▏| 183/999 [02:21<08
[Running Accuracy]: 0.5519,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 183:  18%|▏| 183/999 [02:21<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 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>Compared to the first image, to what extent is the second image affected by blurring?
A. Smaller
B. About the same
C. Bigger
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, to what extent is the second image affected by blurring?
A. Smaller
B. About the same
C. Bigger
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, to what extent is the second image affected by blurring?\nA. Smaller\nB. About the same\nC. Bigger\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([-31.3125], 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]: 0.5519,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 183:  18%|▏| 184/999 [02:22<08:1
[Running Accuracy]: 0.5489,[Response]: C.<|endoftext|>, [Correct Ans]: Smaller, , [Prog]: 184:  18%|▏| 184/999 [02:22<
 {'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, to what extent is the second image affected by blurring?\nA. Smaller\nB. About the same\nC. Bigger\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 is the noise situation in the first image?
A. Similar
B. Much less
C. Much more
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. Similar
B. Much less
C. Much more
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. Similar\nB. Much less\nC. Much more\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([-30.9062], 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]: 0.5489,[Response]: C.<|endoftext|>, [Correct Ans]: Smaller, , [Prog]: 184:  19%|▏| 185/999 [02:22<
[Running Accuracy]: 0.5514,[Response]: B.<|endoftext|>, [Correct Ans]: Much less, , [Prog]: 185:  19%|▏| 185/999 [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>Compared to the second image, how is the noise situation in the first image?\nA. Similar\nB. Much less\nC. Much 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>Are the pixels of the two images both low?
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 pixels of the two images both low?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the pixels of the two images both low?\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([-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]: Much less, , [Prog]: 185:  19%|▏| 186/999 [02:2
[Running Accuracy]: 0.5538,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 186:  19%|▏| 186/999 [02:23<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>Are the pixels of the two images both low?\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 brighter 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 brighter 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 brighter 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([-31.3281], 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]: 0.5538,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 186:  19%|▏| 187/999 [02:23<08:2
[Running Accuracy]: 0.5561,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 187:  19%|▏| 187/999 [02:23<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 color of the first image brighter 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 images affected by reflection?
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 affected by reflection?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both images affected by reflection?\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([-31.2812], 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]: 0.5561,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 187:  19%|▏| 188/999 [02:24<08:2
[Running Accuracy]: 0.5532,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 188:  19%|▏| 188/999 [02:24<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>Are both images affected by reflection?\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 of the second image better than the first 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 illumination of the second image better than the first one?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the illumination of the second image better than the first one?\nA. Yes\nB. No\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([-31.1719], 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]: 0.5532,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 188:  19%|▏| 189/999 [02:25<08:05
[Running Accuracy]: 0.5556,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 189:  19%|▏| 189/999 [02:25<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 illumination of the second image better than the first 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>What kind of distortion is not present in the two images?
A. Blurry
B. Overexposed
C. Low light
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 is not present in the two images?
A. Blurry
B. Overexposed
C. Low light
D. Motion blur
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion is not present in the two images?\nA. Blurry\nB. Overexposed\nC. Low light\nD. 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([-31.0469], 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]: 0.5556,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 189:  19%|▏| 190/999 [02:25<07:5
[Running Accuracy]: 0.5579,[Response]: B.<|endoftext|>, [Correct Ans]: Overexposed, , [Prog]: 190:  19%|▏| 190/999 [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. Blurry\nB. Overexposed\nC. Low light\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>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([-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([-31.3594], 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]: 0.5579,[Response]: B.<|endoftext|>, [Correct Ans]: Overexposed, , [Prog]: 190:  19%|▏| 191/999 [02
[Running Accuracy]: 0.5550,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 191:  19%|▏| 191/999 [02:26<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 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>Compared to the first image, how does the sharpness of the details and textures in the second image differ?
A. Almost 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 does the sharpness of the details and textures in the second image differ?
A. Almost 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 does the sharpness of the details and textures in the second image differ?\nA. Almost the same\nB. Much clearer\nC. Much blurrier\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([-31.5781], 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]: 0.5550,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 191:  19%|▏| 192/999 [02:26<07:3
[Running Accuracy]: 0.5573,[Response]: B.<|endoftext|>, [Correct Ans]: Much clearer, , [Prog]: 192:  19%|▏| 192/999 [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 sharpness of the details and textures in the second image differ?\nA. Almost the same\nB. Much clearer\nC. Much 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>Are the backgrounds of both images blurred?
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 backgrounds of both images blurred?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the backgrounds of both images blurred?\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([-31.0312], 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]: 0.5573,[Response]: B.<|endoftext|>, [Correct Ans]: Much clearer, , [Prog]: 192:  19%|▏| 193/999 [0
[Running Accuracy]: 0.5544,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 193:  19%|▏| 193/999 [02:27<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>Are the backgrounds of both images blurred?\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 level of noise in the second image?
A. Much lighter
B. Much 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 is the level of noise in the second image?
A. Much lighter
B. Much more severe
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the level of noise in the second image?\nA. Much lighter\nB. Much more severe\nC. About the same\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([-30.8281], 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]: 0.5544,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 193:  19%|▏| 194/999 [02:27<07:2
[Running Accuracy]: 0.5567,[Response]: B.<|endoftext|>, [Correct Ans]: Much more severe, , [Prog]: 194:  19%|▏| 194/99
 {'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>Compared to the first image, how is the level of noise in the second image?\nA. Much lighter\nB. Much 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 underexposure?
A. Characters on the building in the second image
B. Building in the second image
C. River in the first image
D. Hippo 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 underexposure?
A. Characters on the building in the second image
B. Building in the second image
C. River in the first image
D. Hippo in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more severely affected by underexposure?\nA. Characters on the building in the second image\nB. Building in the second image\nC. River in the first image\nD. Hippo 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([-31.0156], 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])
 C.

[Running Accuracy]: 0.5567,[Response]: B.<|endoftext|>, [Correct Ans]: Much more severe, , [Prog]: 194:  20%|▏| 195/99
[Running Accuracy]: 0.5538,[Response]: C.<|endoftext|>, [Correct Ans]: Characters on the building 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 severely affected by underexposure?\nA. Characters on the building in the second image\nB. Building in the second image\nC. River in the first image\nD. Hippo 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 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([-31.0469], 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([-31.0469], 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]: 0.5538,[Response]: C.<|endoftext|>, [Correct Ans]: Characters on the building in the second image,
[Running Accuracy]: 0.5510,[Response]: B.<|endoftext|>, [Correct Ans]: Much poorer, , [Prog]: 196:  20%|▏| 196/999 [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>Compared to the second image, is the color of the first image more accurate?
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 accurate?
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 accurate?\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([-31.4531], 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]: 0.5510,[Response]: B.<|endoftext|>, [Correct Ans]: Much poorer, , [Prog]: 196:  20%|▏| 197/999 [02
[Running Accuracy]: 0.5482,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 197:  20%|▏| 197/999 [02:29<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 second image, is the color of the first image more accurate?\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 is the missing distortion?
A. 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:What is the missing distortion?
A. Blur
B. Overexposure
C. Noise
Answer with the option's letter from the given choices directly.

prompts: [["What is the missing distortion?\nA. Blur\nB. Overexposure\nC. Noise\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([-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]: 0.5482,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 197:  20%|▏| 198/999 [02:30<09:1
[Running Accuracy]: 0.5455,[Response]: A.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 198:  20%|▏| 198/999 [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 is the missing distortion?\nA. 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>What distortion is not present in the two images?
A. Low light
B. Blur
C. Underexposure
D. Motion blur
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. Low light
B. Blur
C. Underexposure
D. Motion blur
Answer with the option's letter from the given choices directly.

prompts: [["What distortion is not present in the two images?\nA. Low light\nB. Blur\nC. Underexposure\nD. Motion blur\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([-30.7812], 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]: 0.5455,[Response]: A.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 198:  20%|▏| 199/999 [0
[Running Accuracy]: 0.5427,[Response]: A.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 199:  20%|▏| 199/999 [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 distortion is not present in the two images?\nA. Low light\nB. Blur\nC. Underexposure\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>Which area has the clearest detail and texture?
A. Butterfly in the second image
B. Doll in the first image
C. Dog in the first image
D. Green 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 area has the clearest detail and texture?
A. Butterfly in the second image
B. Doll in the first image
C. Dog in the first image
D. Green leaves in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area has the clearest detail and texture?\nA. Butterfly in the second image\nB. Doll in the first image\nC. Dog in the first image\nD. Green leaves 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([-31.0625], 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]: 0.5427,[Response]: A.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 199:  20%|▏| 200/999 [02
[Running Accuracy]: 0.5450,[Response]: A.<|endoftext|>, [Correct Ans]: Butterfly in the second image, , [Prog]: 200:  
 {'prompt': "A chat between a curious user and an artificial 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 clearest detail and texture?\nA. Butterfly in the second image\nB. Doll in the first image\nC. Dog in the first image\nD. Green 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>What distortion is not present in the two images?
A. Out of focus
B. Blurry
C. Overexposed
D. Underexposed
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. Out of focus
B. Blurry
C. Overexposed
D. Underexposed
Answer with the option's letter from the given choices directly.

prompts: [["What distortion is not present in the two images?\nA. Out of focus\nB. Blurry\nC. Overexposed\nD. Underexposed\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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5450,[Response]: A.<|endoftext|>, [Correct Ans]: Butterfly in the second image, , [Prog]: 200:  
[Running Accuracy]: 0.5423,[Response]: A.<|endoftext|>, [Correct Ans]: Overexposed, , [Prog]: 201:  20%|▏| 201/999 [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 distortion is not present in the two images?\nA. Out of focus\nB. Blurry\nC. Overexposed\nD. 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 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([-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]: 0.5423,[Response]: A.<|endoftext|>, [Correct Ans]: Overexposed, , [Prog]: 201:  20%|▏| 202/999 [02
[Running Accuracy]: 0.5446,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 202:  20%|▏| 202/999 [02:33<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 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>Are the details and textures in the first image much sharper 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:Are the details and textures in the first image much sharper than 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 much sharper than in 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([-31.1250], 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]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 202:  20%|▏| 203/999 [02:33<07:4
[Running Accuracy]: 0.5468,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 203:  20%|▏| 203/999 [02:33<07:44
 {'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 the details and textures in the first image much sharper 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>Compared to the first image, how is the overexposure impact on the second image?
A. Slightly more
B. About the same
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 is the overexposure impact on the second image?
A. Slightly more
B. About the same
C. More severe
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the overexposure impact on the second image?\nA. Slightly more\nB. About the same\nC. More severe\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([-30.8125], 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])
 C.

[Running Accuracy]: 0.5468,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 203:  20%|▏| 204/999 [02:34<08:45
[Running Accuracy]: 0.5490,[Response]: C.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 204:  20%|▏| 204/999 [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 overexposure impact on the second image?\nA. Slightly more\nB. About the same\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 noise situation 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 the noise situation more severe in the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the noise situation more severe in the first 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([-31.0156], 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]: 0.5490,[Response]: C.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 204:  21%|▏| 205/999 [02
[Running Accuracy]: 0.5512,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 205:  21%|▏| 205/999 [02:35<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 noise situation more severe 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>Compared to the first image, how does the noise level in the second image compare?
A. More
B. About the same
C. 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 does the noise level in the second image compare?
A. More
B. About the same
C. Less
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how does the noise level in the second image compare?\nA. More\nB. About the same\nC. Less\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([-30.8594], 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]: 0.5512,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 205:  21%|▏| 206/999 [02:35<07:5
[Running Accuracy]: 0.5485,[Response]: A.<|endoftext|>, [Correct Ans]: Less, , [Prog]: 206:  21%|▏| 206/999 [02:35<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 does the noise level in the second image compare?\nA. More\nB. About the same\nC. Less\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 has clearer details and texture?
A. The green leaves in the first image
B. The roof tiles in the first image
C. The fish scales 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 has clearer details and texture?
A. The green leaves in the first image
B. The roof tiles in the first image
C. The fish scales in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area in the two images has clearer details and texture?\nA. The green leaves in the first image\nB. The roof tiles in the first image\nC. The fish scales 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([-30.4844], 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]: 0.5485,[Response]: A.<|endoftext|>, [Correct Ans]: Less, , [Prog]: 206:  21%|▏| 207/999 [02:36<07:
[Running Accuracy]: 0.5459,[Response]: B.<|endoftext|>, [Correct Ans]: The fish scales 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 in the two images has clearer details and texture?\nA. The green leaves in the first image\nB. The roof tiles in the first image\nC. The fish scales 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 has clearer details and texture?
A. The red part in the first image
B. The water surface in the second image
C. The black flower in the middle of the first image
D. The rocks 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 texture?
A. The red part in the first image
B. The water surface in the second image
C. The black flower in the middle of the first image
D. The rocks in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area has clearer details and texture?\nA. The red part in the first image\nB. The water surface in the second image\nC. The black flower in the middle of the first image\nD. The rocks 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([-31.2188], 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]: 0.5459,[Response]: B.<|endoftext|>, [Correct Ans]: The fish scales in the second image, , [Prog]: 
[Running Accuracy]: 0.5433,[Response]: B.<|endoftext|>, [Correct Ans]: The red part in the first image, , [Prog]: 208:
 {'prompt': "A chat between a curious user and an artificial 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 texture?\nA. The red part in the first image\nB. The water surface in the second image\nC. The black flower in the middle of the first image\nD. The rocks 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 in the two images is most affected by blurring?
A. Reptile in the second image
B. Man's face in the first image
C. Background in the second image
D. 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 area in the two images is most affected by blurring?
A. Reptile in the second image
B. Man's face in the first image
C. Background in the second image
D. Background in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area in the two images is most affected by blurring?\nA. Reptile in the second image\nB. Man's face in the first image\nC. Background in the second image\nD. Background 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([-31.0156], 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])
 C.

[Running Accuracy]: 0.5433,[Response]: B.<|endoftext|>, [Correct Ans]: The red part in the first image, , [Prog]: 208:
[Running Accuracy]: 0.5455,[Response]: C.<|endoftext|>, [Correct Ans]: Background in the second image, , [Prog]: 209: 
 {'prompt': "A chat between a curious user and an artificial 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 most affected by blurring?\nA. Reptile in the second image\nB. Man's face in the first image\nC. Background in the second image\nD. Background 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 area in the two images has clearer details and textures?
A. The flow of cars in the first image
B. The lens in the second image
C. The desktop in the second image
D. 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 area in the two images has clearer details and textures?
A. The flow of cars in the first image
B. The lens in the second image
C. The desktop in the second image
D. The building in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area in the two images has clearer details and textures?\nA. The flow of cars in the first image\nB. The lens in the second image\nC. The desktop in the second image\nD. The building 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([-31.2812], 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]: 0.5455,[Response]: C.<|endoftext|>, [Correct Ans]: Background in the second image, , [Prog]: 209: 
[Running Accuracy]: 0.5476,[Response]: B.<|endoftext|>, [Correct Ans]: The lens in the second image, , [Prog]: 210:  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 in the two images has clearer details and textures?\nA. The flow of cars in the first image\nB. The lens in the second image\nC. The desktop in the second image\nD. 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 area is more severely affected by blur?
A. Grassland in the first image
B. Man's face in the second image
C. Man's body in the second image
D. Zebra 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 blur?
A. Grassland in the first image
B. Man's face in the second image
C. Man's body in the second image
D. Zebra in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more severely affected by blur?\nA. Grassland in the first image\nB. Man's face in the second image\nC. Man's body in the second image\nD. Zebra 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([-30.9375], 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]: 0.5476,[Response]: B.<|endoftext|>, [Correct Ans]: The lens in the second image, , [Prog]: 210:  2
[Running Accuracy]: 0.5498,[Response]: B.<|endoftext|>, [Correct Ans]: Man's face in the second image, , [Prog]: 211: 
 {'prompt': "A chat between a curious user and an artificial 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 blur?\nA. Grassland in the first image\nB. Man's face in the second image\nC. Man's body in the second image\nD. Zebra 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 much is the influence of snowflake distortion on the first image?
A. Almost the same
B. Slightly more
C. More serious
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 much is the influence of snowflake distortion on the first image?
A. Almost the same
B. Slightly more
C. More serious
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how much is the influence of snowflake distortion on the first image?\nA. Almost the same\nB. Slightly more\nC. More serious\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([-31.1875], 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]: 0.5498,[Response]: B.<|endoftext|>, [Correct Ans]: Man's face in the second image, , [Prog]: 211: 
[Running Accuracy]: 0.5519,[Response]: C.<|endoftext|>, [Correct Ans]: More serious, , [Prog]: 212:  21%|▏| 212/999 [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 much is the influence of snowflake distortion on the first image?\nA. Almost the same\nB. Slightly more\nC. More serious\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 the clearest fine texture?
A. The stool in the second image
B. The ornament toy in the first image
C. The parasol in the second image
D. The partial steering wheel 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 has the clearest fine texture?
A. The stool in the second image
B. The ornament toy in the first image
C. The parasol in the second image
D. The partial steering wheel in front of the lens in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area has the clearest fine texture?\nA. The stool in the second image\nB. The ornament toy in the first image\nC. The parasol in the second image\nD. The partial steering wheel in front of the lens in the first image\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([-31.1250], 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]: 0.5519,[Response]: C.<|endoftext|>, [Correct Ans]: More serious, , [Prog]: 212:  21%|▏| 213/999 [0
[Running Accuracy]: 0.5493,[Response]: A.<|endoftext|>, [Correct Ans]: The partial steering wheel in front of the lens
 {'prompt': "A chat between a curious user and an artificial 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 clearest fine texture?\nA. The stool in the second image\nB. The ornament toy in the first image\nC. The parasol in the second image\nD. The partial steering wheel 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>Which area is more affected by overexposure?
A. The road surface in the first image
B. The grassland in the first image
C. The dancers in the second image
D. The curtain backdrop 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 overexposure?
A. The road surface in the first image
B. The grassland in the first image
C. The dancers in the second image
D. The curtain backdrop in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more affected by overexposure?\nA. The road surface in the first image\nB. The grassland in the first image\nC. The dancers in the second image\nD. The curtain backdrop 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([-31.1250], 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]: 0.5493,[Response]: A.<|endoftext|>, [Correct Ans]: The partial steering wheel in front of the lens
[Running Accuracy]: 0.5467,[Response]: C.<|endoftext|>, [Correct Ans]: The road surface 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 area is more affected by overexposure?\nA. The road surface in the first image\nB. The grassland in the first image\nC. The dancers in the second image\nD. The curtain backdrop 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 area has clearer details and textures?
A. The bedsheet in the first image
B. The carpet in the first image
C. The soccer field turf in the second image
D. The audience crowd 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 bedsheet in the first image
B. The carpet in the first image
C. The soccer field turf in the second image
D. The audience crowd 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 bedsheet in the first image\nB. The carpet in the first image\nC. The soccer field turf in the second image\nD. The audience crowd in the second 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([-31.4531], 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]: 0.5467,[Response]: C.<|endoftext|>, [Correct Ans]: The road surface in the first image, , [Prog]: 
[Running Accuracy]: 0.5442,[Response]: C.<|endoftext|>, [Correct Ans]: The carpet in the first image, , [Prog]: 215:  
 {'prompt': "A chat between a curious user and an artificial 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 bedsheet in the first image\nB. The carpet in the first image\nC. The soccer field turf in the second image\nD. The audience crowd 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. 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 clarity 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 clarity 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([-30.6562], 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([-30.8750], 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]: 0.5442,[Response]: C.<|endoftext|>, [Correct Ans]: The carpet in the first image, , [Prog]: 215:  
[Running Accuracy]: 0.5463,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 216:  22%|▏| 216/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 higher\nB. Much lower\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>Have both images been affected by trailing shadow and 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 images been affected by trailing shadow and blur?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Have both images been affected by trailing shadow and blur?\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([-30.8281], 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]: 0.5463,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 216:  22%|▏| 217/999 
[Running Accuracy]: 0.5438,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 217:  22%|▏| 217/999 [02:41<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>Have both images been affected by trailing shadow and 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>Is the texture detail of 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 texture detail of 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 texture detail of 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([-31.4375], 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([-31.4062], 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]: 0.5438,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 217:  22%|▏| 218/999 [02:42<07:15
[Running Accuracy]: 0.5459,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 218:  22%|▏| 218/999 [02:42<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>Is the texture detail of 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>Compared to the second image, how clear is the texture detail in 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:Compared to the second image, how clear is the texture detail in the first image?
A. Similar
B. Clearer
C. Blurrier
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how clear is the texture detail in the first image?\nA. Similar\nB. Clearer\nC. Blurrier\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([-31.2344], 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]: 0.5459,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 218:  22%|▏| 219/999 [02:42<07:12
[Running Accuracy]: 0.5479,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 219:  22%|▏| 219/999 [02:42<
 {'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>Compared to the second image, how clear is the texture detail in the first 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 area has clearer details and textures?
A. The street lamp in the second image
B. The clouds in the sky in the second image
C. The purple shirt in the first image
D. The red flower 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 street lamp in the second image
B. The clouds in the sky in the second image
C. The purple shirt in the first image
D. The red flower 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 street lamp in the second image\nB. The clouds in the sky in the second image\nC. The purple shirt in the first image\nD. The red flower in the first 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([-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]: 0.5479,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 219:  22%|▏| 220/999 [02:43<
[Running Accuracy]: 0.5500,[Response]: B.<|endoftext|>, [Correct Ans]: The clouds in the sky 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 area has clearer details and textures?\nA. The street lamp in the second image\nB. The clouds in the sky in the second image\nC. The purple shirt in the first image\nD. The red flower 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 exposure of the first image 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 exposure of the first image worse than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the exposure of the first image worse 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([-30.6094], 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]: 0.5500,[Response]: B.<|endoftext|>, [Correct Ans]: The clouds in the sky in the second image, , [P
[Running Accuracy]: 0.5520,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 221:  22%|▏| 221/999 [02:43<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 exposure of the first image 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>Compared to the second image, how is the noise situation in the first image?
A. Much milder
B. Much 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 second image, how is the noise situation in the first image?
A. Much milder
B. Much more severe
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 milder\nB. Much more severe\nC. About the same\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([-31.1719], 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]: 0.5520,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 221:  22%|▏| 222/999 [02:44<07:0
[Running Accuracy]: 0.5541,[Response]: B.<|endoftext|>, [Correct Ans]: Much more severe, , [Prog]: 222:  22%|▏| 222/99
 {'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>Compared to the second image, how is the noise situation in the first image?\nA. Much milder\nB. Much 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 has clearer details and textures?
A. Keyboard in the first image
B. Bread in the first image
C. Earphone earmuff in the second image
D. Headband of earphone 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. Keyboard in the first image
B. Bread in the first image
C. Earphone earmuff in the second image
D. Headband of earphone in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area has clearer details and textures?\nA. Keyboard in the first image\nB. Bread in the first image\nC. Earphone earmuff in the second image\nD. Headband of earphone 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([-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]: 0.5541,[Response]: B.<|endoftext|>, [Correct Ans]: Much more severe, , [Prog]: 222:  22%|▏| 223/99
[Running Accuracy]: 0.5561,[Response]: C.<|endoftext|>, [Correct Ans]: Earphone earmuff 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 details and textures?\nA. Keyboard in the first image\nB. Bread in the first image\nC. Earphone earmuff in the second image\nD. Headband of earphone 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 area has the clearest fine texture?
A. The trunk in the second image
B. The grass in the second image
C. The blanket in the first image
D. 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 area has the clearest fine texture?
A. The trunk in the second image
B. The grass in the second image
C. The blanket in the first image
D. The dog in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area has the clearest fine texture?\nA. The trunk in the second image\nB. The grass in the second image\nC. The blanket in the first image\nD. The dog in the first 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([-31.4688], 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]: 0.5561,[Response]: C.<|endoftext|>, [Correct Ans]: Earphone earmuff in the second image, , [Prog]:
[Running Accuracy]: 0.5536,[Response]: A.<|endoftext|>, [Correct Ans]: The dog in the first image, , [Prog]: 224:  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>Which area has the clearest fine texture?\nA. The trunk in the second image\nB. The grass in the second image\nC. The blanket in the first image\nD. 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>Which area is more affected by the blur?
A. Grassland in the first image
B. Black vehicle in the middle of the second image
C. Motorcycle at the back of the second image
D. Rabbit 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 the blur?
A. Grassland in the first image
B. Black vehicle in the middle of the second image
C. Motorcycle at the back of the second image
D. Rabbit in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more affected by the blur?\nA. Grassland in the first image\nB. Black vehicle in the middle of the second image\nC. Motorcycle at the back of the second image\nD. Rabbit in the first image\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([-31.0156], 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]: 0.5536,[Response]: A.<|endoftext|>, [Correct Ans]: The dog in the first image, , [Prog]: 224:  23%
[Running Accuracy]: 0.5556,[Response]: B.<|endoftext|>, [Correct Ans]: Black vehicle in the middle 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 is more affected by the blur?\nA. Grassland in the first image\nB. Black vehicle in the middle of the second image\nC. Motorcycle at the back of the second image\nD. Rabbit 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 area is more severely affected by underexposure?
A. Subway in the first image
B. Lower part of the door in the second image
C. Person in the first image
D. Signboard 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 underexposure?
A. Subway in the first image
B. Lower part of the door in the second image
C. Person in the first image
D. Signboard in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more severely affected by underexposure?\nA. Subway in the first image\nB. Lower part of the door in the second image\nC. Person in the first image\nD. Signboard 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([-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]: 0.5556,[Response]: B.<|endoftext|>, [Correct Ans]: Black vehicle in the middle of the second image
[Running Accuracy]: 0.5575,[Response]: B.<|endoftext|>, [Correct Ans]: Lower part of the door 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 severely affected by underexposure?\nA. Subway in the first image\nB. Lower part of the door in the second image\nC. Person in the first image\nD. Signboard 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 clear is the detailed texture of the main subject 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 clear is the detailed texture of the main subject 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 clear is the detailed texture of the main subject 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([-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([-31.3438], 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]: Lower part of the door in the second image, , [
[Running Accuracy]: 0.5595,[Response]: A.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 227:  23%|▏| 227/999 [02:47<
 {'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 clear is the detailed texture of the main subject 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>Compared to the second image, how is the clarity of 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 is the clarity of 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 is the clarity of 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([-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])
 C.

[Running Accuracy]: 0.5595,[Response]: A.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 227:  23%|▏| 228/999 [02:48<
[Running Accuracy]: 0.5614,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 228:  23%|▏| 228/999 [02:48<
 {'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 second image, how is the clarity of 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': '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 much 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 much 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 much clearer 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])
 A.

[Running Accuracy]: 0.5614,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 228:  23%|▏| 229/999 [02:48<
[Running Accuracy]: 0.5633,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 229:  23%|▏| 229/999 [02:48<07:42
 {'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 much 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>Compared to the first image, how is the focus of the second image?
A. About the same
B. Slightly worse
C. Slightly 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 focus of the second image?
A. About the same
B. Slightly worse
C. Slightly better
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. About the same\nB. Slightly worse\nC. Slightly better\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([-30.8750], 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]: 0.5633,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 229:  23%|▏| 230/999 [02:49<08:26
[Running Accuracy]: 0.5652,[Response]: C.<|endoftext|>, [Correct Ans]: Slightly better, , [Prog]: 230:  23%|▏| 230/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. About the same\nB. Slightly worse\nC. Slightly 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 is the most significant issue between the first image and the second image?
A. Noise
B. Overexposure
C. Underexposure
D. Low light
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What is the most significant issue between the first image and the second image?
A. Noise
B. Overexposure
C. Underexposure
D. Low light
Answer with the option's letter from the given choices directly.

prompts: [["What is the most significant issue between the first image and the second image?\nA. Noise\nB. Overexposure\nC. Underexposure\nD. Low light\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([-31.1875], 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]: 0.5652,[Response]: C.<|endoftext|>, [Correct Ans]: Slightly better, , [Prog]: 230:  23%|▏| 231/999
[Running Accuracy]: 0.5671,[Response]: A.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 231:  23%|▏| 231/999 [02:50<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 is the most significant issue between the first image and the second image?\nA. Noise\nB. Overexposure\nC. Underexposure\nD. Low light\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 most obvious issue between the first image and the second image?
A. Not realistic
B. Out of focus
C. Motion blur
D. Low light
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What is the most obvious issue between the first image and the second image?
A. Not realistic
B. Out of focus
C. Motion blur
D. Low light
Answer with the option's letter from the given choices directly.

prompts: [["What is the most obvious issue between the first image and the second image?\nA. Not realistic\nB. Out of focus\nC. Motion blur\nD. Low light\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([-31.1406], 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]: 0.5671,[Response]: A.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 231:  23%|▏| 232/999 [02:50<07
[Running Accuracy]: 0.5647,[Response]: B.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 232:  23%|▏| 232/999 [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 most obvious issue between the first image and the second image?\nA. Not realistic\nB. Out of focus\nC. Motion blur\nD. Low 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>Compared to the second image, how is the sharpness of the first image?
A. Much lower
B. Similar
C. Much 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 is the sharpness of the first image?
A. Much lower
B. Similar
C. Much higher
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. Much lower\nB. Similar\nC. Much higher\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([-30.7500], 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]: 0.5647,[Response]: B.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 232:  23%|▏| 233/999 [02
[Running Accuracy]: 0.5665,[Response]: A.<|endoftext|>, [Correct Ans]: Much lower, , [Prog]: 233:  23%|▏| 233/999 [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 sharpness of the first image?\nA. Much lower\nB. Similar\nC. Much 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>Are there obvious color distortions in 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 there obvious color distortions in both images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are there obvious color distortions in both 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([-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]: 0.5665,[Response]: A.<|endoftext|>, [Correct Ans]: Much lower, , [Prog]: 233:  23%|▏| 234/999 [02:
[Running Accuracy]: 0.5684,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 234:  23%|▏| 234/999 [02:51<07:21
 {'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 there obvious color distortions in 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 area is more severely affected by underexposure?
A. The cake in the first image
B. The person in the first image
C. The door in the second image
D. The two 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 area is more severely affected by underexposure?
A. The cake in the first image
B. The person in the first image
C. The door in the second image
D. The two people in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more severely affected by underexposure?\nA. The cake in the first image\nB. The person in the first image\nC. The door in the second image\nD. The two people 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([-31.2500], 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]: 0.5684,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 234:  24%|▏| 235/999 [02:52<07:16
[Running Accuracy]: 0.5660,[Response]: C.<|endoftext|>, [Correct Ans]: The person in the first image, , [Prog]: 235:  
 {'prompt': "A chat between a curious user and an artificial 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 underexposure?\nA. The cake in the first image\nB. The person in the first image\nC. The door in the second image\nD. The two people 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 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([-31.1406], 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([-31.2656], 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]: 0.5660,[Response]: C.<|endoftext|>, [Correct Ans]: The person in the first image, , [Prog]: 235:  
[Running Accuracy]: 0.5678,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 236:  24%|▏| 236/999 [02:52<07:20
 {'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 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 second image, how is the first image affected by ghosting?
A. Similar
B. Slightly more
C. Significantly more
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 ghosting?
A. Similar
B. Slightly more
C. Significantly more
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how is the first image affected by ghosting?\nA. Similar\nB. Slightly more\nC. Significantly more\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([-30.1406], 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]: 0.5678,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 236:  24%|▏| 237/999 [02:53<07:20
[Running Accuracy]: 0.5696,[Response]: C.<|endoftext|>, [Correct Ans]: Significantly more, , [Prog]: 237:  24%|▏| 237/
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 ghosting?\nA. Similar\nB. Slightly more\nC. Significantly 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 second image, how is the clarity of the first image?
A. Slightly higher
B. Slightly 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 second image, how is the clarity of the first image?
A. Slightly higher
B. Slightly lower
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 higher\nB. Slightly lower\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([-31.1250], 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]: 0.5696,[Response]: C.<|endoftext|>, [Correct Ans]: Significantly more, , [Prog]: 237:  24%|▏| 238/
[Running Accuracy]: 0.5714,[Response]: B.<|endoftext|>, [Correct Ans]: Slightly lower, , [Prog]: 238:  24%|▏| 238/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 higher\nB. Slightly 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 color of picture 2 richer than picture 1?
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 picture 2 richer than picture 1?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of picture 2 richer than picture 1?\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([-31.0938], 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]: 0.5714,[Response]: B.<|endoftext|>, [Correct Ans]: Slightly lower, , [Prog]: 238:  24%|▏| 239/999 
[Running Accuracy]: 0.5690,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 239:  24%|▏| 239/999 [02:54<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 color of picture 2 richer than picture 1?\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 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 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 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([-30.5625], 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([-31.0625], 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]: 0.5690,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 239:  24%|▏| 240/999 [02:55<07:2
[Running Accuracy]: 0.5708,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 240:  24%|▏| 240/999 [02:55<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 noise in the first image 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>Compared to the second image, how is the first image affected by overexposure?
A. Less severe
B. About the same
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 overexposure?
A. Less severe
B. About the same
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 overexposure?\nA. Less severe\nB. About the same\nC. More severe\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]: 0.5708,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 240:  24%|▏| 241/999 [02:55<07:3
[Running Accuracy]: 0.5685,[Response]: C.<|endoftext|>, [Correct Ans]: Less severe, , [Prog]: 241:  24%|▏| 241/999 [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 overexposure?\nA. Less severe\nB. About the same\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 second image more severely affected by underexposure 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 severely affected by underexposure 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 severely affected by underexposure than the first 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([-31.5000], 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]: 0.5685,[Response]: C.<|endoftext|>, [Correct Ans]: Less severe, , [Prog]: 241:  24%|▏| 242/999 [02
[Running Accuracy]: 0.5661,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 242:  24%|▏| 242/999 [02:56<08:14
 {'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 second image more severely affected by underexposure 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 motion blur in the first image more severe?
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 motion blur in the first image more severe?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the motion blur in the first image more severe?\nA. No\nB. Yes\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([-31.2344], 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]: 0.5661,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 242:  24%|▏| 243/999 [02:57<08:06
[Running Accuracy]: 0.5679,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 243:  24%|▏| 243/999 [02:57<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 motion blur in the first image more severe?\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 second image much 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 focus of the second image much better than the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the focus of the second image much better than the first image?\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([-31.0625], 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]: 0.5679,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 243:  24%|▏| 244/999 [02:57<08:0
[Running Accuracy]: 0.5697,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 244:  24%|▏| 244/999 [02:57<08:05
 {'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 focus of the second image much 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 clarity 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 clarity 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 clarity 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([-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([-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]: 0.5697,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 244:  25%|▏| 245/999 [02:58<08:07
[Running Accuracy]: 0.5714,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 245:  25%|▏| 245/999 [02:58<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 clarity 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 noise in the first image significantly less 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 significantly less 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 significantly less than in the second image?\nA. Yes\nB. No\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([-30.9531], 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]: 0.5714,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 245:  25%|▏| 246/999 [02:59<08:2
[Running Accuracy]: 0.5691,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 246:  25%|▏| 246/999 [02:59<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>Is the noise in the first image significantly less 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 detail texture in the first image clearer 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 detail texture in the first image clearer than in the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the detail texture in the first image clearer than in the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
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])
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]: 0.5691,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 246:  25%|▏| 247/999 [03:00<09:20
[Running Accuracy]: 0.5709,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 247:  25%|▏| 247/999 [03:00<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>Is the detail texture in the first image clearer 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 color in the first image richer 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 color in the first image richer than in the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the color in the first image richer than in the second image?\nA. No\nB. Yes\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([-30.7188], 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]: 0.5709,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 247:  25%|▏| 248/999 [03:01<09:5
[Running Accuracy]: 0.5685,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 248:  25%|▏| 248/999 [03:01<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 color in the first image richer 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 motion blur in the first image much 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 motion blur in the first image much 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 motion blur in the first image much 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([-31.5000], 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([-31.1719], 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]: 0.5685,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 248:  25%|▏| 249/999 [03:01<10:09
[Running Accuracy]: 0.5663,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 249:  25%|▏| 249/999 [03:01<10:09
 {'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 motion blur in the first image much 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>Is the overexposure of 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 overexposure of 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 overexposure of 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([-31.4688], 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([-31.0781], 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]: 0.5663,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 249:  25%|▎| 250/999 [03:02<10:26
[Running Accuracy]: 0.5680,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 250:  25%|▎| 250/999 [03:02<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 overexposure of 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 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 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 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([-31.3281], 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([-31.2656], 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]: 0.5680,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 250:  25%|▎| 251/999 [03:03<10:4
[Running Accuracy]: 0.5657,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 251:  25%|▎| 251/999 [03:03<10:4
 {'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 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': 'B.<|endoftext|>'} 

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

prompts: [["Is the blurriness of the first image much more severe 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([-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]: 0.5657,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 251:  25%|▎| 252/999 [03:04<10:5
[Running Accuracy]: 0.5675,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 252:  25%|▎| 252/999 [03:04<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>Is the blurriness of the first image much more severe 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 is not included in the obvious more serious distortion between the first image and the second image?
A. Glare
B. Motion blur
C. Underexposure
D. Low 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 is not included in the obvious more serious distortion between the first image and the second image?
A. Glare
B. Motion blur
C. Underexposure
D. Low light
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following is not included in the obvious more serious distortion between the first image and the second image?\nA. Glare\nB. Motion blur\nC. Underexposure\nD. Low light\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([-30.7344], 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]: 0.5675,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 252:  25%|▎| 253/999 [03:05<09:5
[Running Accuracy]: 0.5652,[Response]: C.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 253:  25%|▎| 253/999 [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>Which of the following is not included in the obvious more serious distortion between the first image and the second image?\nA. Glare\nB. Motion blur\nC. Underexposure\nD. Low light\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 is the clarity of the details and textures in the first 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 second image, how is the clarity of the details and textures in the first 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 second image, how is the clarity of the details and textures in the first 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([-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([-31.1406], 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]: 0.5652,[Response]: C.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 253:  25%|▎| 254/999 [03
[Running Accuracy]: 0.5630,[Response]: C.<|endoftext|>, [Correct Ans]: Much blurrier, , [Prog]: 254:  25%|▎| 254/999 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 details and textures in the first 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': '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 focus of the first image noticeably inferior 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 focus of the first image noticeably inferior to 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 noticeably inferior to 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([-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]: 0.5630,[Response]: C.<|endoftext|>, [Correct Ans]: Much blurrier, , [Prog]: 254:  26%|▎| 255/999 [
[Running Accuracy]: 0.5647,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 255:  26%|▎| 255/999 [03:06<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 focus of the first image noticeably inferior 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>Compared to figure two, how is the situation of noise points in figure one?
A. Much lighter
B. Much 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 figure two, how is the situation of noise points in figure one?
A. Much lighter
B. Much more severe
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to figure two, how is the situation of noise points in figure one?\nA. Much lighter\nB. Much more severe\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([-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]: 0.5647,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 255:  26%|▎| 256/999 [03:07<08:3
[Running Accuracy]: 0.5664,[Response]: B.<|endoftext|>, [Correct Ans]: Much more severe, , [Prog]: 256:  26%|▎| 256/99
 {'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>Compared to figure two, how is the situation of noise points in figure one?\nA. Much lighter\nB. Much 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 has more severe motion blur?
A. The starfish pendant in the second image
B. The man's face in the first image
C. The woman's face 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 area has more severe motion blur?
A. The starfish pendant in the second image
B. The man's face in the first image
C. The woman's face in the first image
D. The background in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which area has more severe motion blur?\nA. The starfish pendant in the second image\nB. The man's face in the first image\nC. The woman's face 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([-30.8906], 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([-31.2969], 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]: 0.5664,[Response]: B.<|endoftext|>, [Correct Ans]: Much more severe, , [Prog]: 256:  26%|▎| 257/99
[Running Accuracy]: 0.5681,[Response]: B.<|endoftext|>, [Correct Ans]: The man's face 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 area has more severe motion blur?\nA. The starfish pendant in the second image\nB. The man's face in the first image\nC. The woman's face 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 more severe motion blur?
A. Escalator in the second image
B. Convenience store sign in the first image
C. Shop in the second image
D. Vehicle 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 has more severe motion blur?
A. Escalator in the second image
B. Convenience store sign in the first image
C. Shop in the second image
D. Vehicle in front of the lens in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area has more severe motion blur?\nA. Escalator in the second image\nB. Convenience store sign in the first image\nC. Shop in the second image\nD. Vehicle in front of the lens 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([-31.0469], 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])
 D.

[Running Accuracy]: 0.5681,[Response]: B.<|endoftext|>, [Correct Ans]: The man's face in the first image, , [Prog]: 25
[Running Accuracy]: 0.5698,[Response]: D.<|endoftext|>, [Correct Ans]: Vehicle in front of the lens 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 area has more severe motion blur?\nA. Escalator in the second image\nB. Convenience store sign in the first image\nC. Shop in the second image\nD. Vehicle in front of the lens 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 of the following is not included in the more severe distortion of the first image compared to the second image?
A. Noise
B. Blur
C. Low Sharpness
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 is not included in the more severe distortion of the first image compared to the second image?
A. Noise
B. Blur
C. Low Sharpness
D. Motion Blur
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following is not included in the more severe distortion of the first image compared to the second image?\nA. Noise\nB. Blur\nC. Low Sharpness\nD. Motion Blur\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])
 D.

[Running Accuracy]: 0.5698,[Response]: D.<|endoftext|>, [Correct Ans]: Vehicle in front of the lens in the first image
[Running Accuracy]: 0.5676,[Response]: D.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 259:  26%|▎| 259/999 [03:09<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 included in the more severe distortion of the first image compared to the second image?\nA. Noise\nB. Blur\nC. Low Sharpness\nD. Motion Blur\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>How does the noise situation in the second image compare to that in the first image?
A. similar
B. much worse
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 that in the first image?
A. similar
B. much worse
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 that in the first image?\nA. similar\nB. much worse\nC. much slighter\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([-30.6094], 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]: 0.5676,[Response]: D.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 259:  26%|▎| 260/999 [03:09<07
[Running Accuracy]: 0.5692,[Response]: B.<|endoftext|>, [Correct Ans]: much worse, , [Prog]: 260:  26%|▎| 260/999 [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 noise situation in the second image compare to that in the first image?\nA. similar\nB. much worse\nC. much 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>Which of the following is not a significantly severe problem in the second image compared to the first image?
A. Blur
B. Low light
C. Out of focus
D. Overexposure
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 significantly severe problem in the second image compared to the first image?
A. Blur
B. Low light
C. Out of focus
D. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following is not a significantly severe problem in the second image compared to the first image?\nA. Blur\nB. Low light\nC. Out of focus\nD. Overexposure\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([-30.7812], 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]: 0.5692,[Response]: B.<|endoftext|>, [Correct Ans]: much worse, , [Prog]: 260:  26%|▎| 261/999 [03:
[Running Accuracy]: 0.5709,[Response]: B.<|endoftext|>, [Correct Ans]: Low light, , [Prog]: 261:  26%|▎| 261/999 [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>Which of the following is not a significantly severe problem in the second image compared to the first image?\nA. Blur\nB. Low light\nC. Out of focus\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>Which of the following is not included in the obviously more serious problem of the second image than the first image?
A. halo
B. low light
C. underexposure
D. unreal
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 obviously more serious problem of the second image than the first image?
A. halo
B. low light
C. underexposure
D. unreal
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following is not included in the obviously more serious problem of the second image than the first image?\nA. halo\nB. low light\nC. underexposure\nD. unreal\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])
 C.

[Running Accuracy]: 0.5709,[Response]: B.<|endoftext|>, [Correct Ans]: Low light, , [Prog]: 261:  26%|▎| 262/999 [03:1
[Running Accuracy]: 0.5687,[Response]: C.<|endoftext|>, [Correct Ans]: unreal, , [Prog]: 262:  26%|▎| 262/999 [03:11<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>Which of the following is not included in the obviously more serious problem of the second image than the first image?\nA. halo\nB. low light\nC. underexposure\nD. unreal\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 not a significantly more serious problem in the second image than in the first image?
A. overexposure
B. underexposure
C. noise
D. obstruction
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 significantly more serious problem in the second image than in the first image?
A. overexposure
B. underexposure
C. noise
D. obstruction
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following is not a significantly more serious problem in the second image than in the first image?\nA. overexposure\nB. underexposure\nC. noise\nD. obstruction\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([-31.1719], 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]: 0.5687,[Response]: C.<|endoftext|>, [Correct Ans]: unreal, , [Prog]: 262:  26%|▎| 263/999 [03:11<0
[Running Accuracy]: 0.5665,[Response]: D.<|endoftext|>, [Correct Ans]: underexposure, , [Prog]: 263:  26%|▎| 263/999 [
 {'prompt': "A chat between a curious user and an artificial 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 significantly more serious problem in the second image than in the first image?\nA. overexposure\nB. underexposure\nC. noise\nD. obstruction\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 in 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 in 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 in 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([-31.1875], 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([-31.2969], 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]: 0.5665,[Response]: D.<|endoftext|>, [Correct Ans]: underexposure, , [Prog]: 263:  26%|▎| 264/999 [
[Running Accuracy]: 0.5644,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 264:  26%|▎| 264/999 [03:12<08:14
 {'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 color in 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>Compared to the first image, how is the clarity of the second image?
A. Lower
B. About the same
C. Higher
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. About the same
C. Higher
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. About the same\nC. Higher\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([-31.0625], 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]: 0.5644,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 264:  27%|▎| 265/999 [03:13<08:17
[Running Accuracy]: 0.5660,[Response]: A.<|endoftext|>, [Correct Ans]: Lower, , [Prog]: 265:  27%|▎| 265/999 [03:13<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>Compared to the first image, how is the clarity of the second image?\nA. Lower\nB. About the same\nC. 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>Compared to the first image, how is the clarity of the details and textures in the second image?
A. Cleaner
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 details and textures in the second image?
A. Cleaner
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 details and textures in the second image?\nA. Cleaner\nB. About the same\nC. More blurry\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([-31.3594], 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]: 0.5660,[Response]: A.<|endoftext|>, [Correct Ans]: Lower, , [Prog]: 265:  27%|▎| 266/999 [03:13<08
[Running Accuracy]: 0.5677,[Response]: A.<|endoftext|>, [Correct Ans]: Cleaner, , [Prog]: 266:  27%|▎| 266/999 [03:13<
 {'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 image, how is the clarity of the details and textures in the second image?\nA. Cleaner\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>Compared to the first image, how much is the second image affected by motion blur?
A. Similar
B. Slightly severe
C. Slightly mild
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. Similar
B. Slightly severe
C. Slightly mild
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. Similar\nB. Slightly severe\nC. Slightly mild\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([-31.2656], 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]: 0.5677,[Response]: A.<|endoftext|>, [Correct Ans]: Cleaner, , [Prog]: 266:  27%|▎| 267/999 [03:14<
[Running Accuracy]: 0.5655,[Response]: B.<|endoftext|>, [Correct Ans]: Slightly mild, , [Prog]: 267:  27%|▎| 267/999 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. Similar\nB. Slightly severe\nC. Slightly mild\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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 texture in the second image?
A. 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 texture in the second image?
A. 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 texture in the second image?\nA. Blurry\nB. Clearer\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([-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])
 B.

[Running Accuracy]: 0.5655,[Response]: B.<|endoftext|>, [Correct Ans]: Slightly mild, , [Prog]: 267:  27%|▎| 268/999 [
[Running Accuracy]: 0.5672,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 268:  27%|▎| 268/999 [03:15<
 {'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 texture in the second image?\nA. 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 impact of underexposure more severe 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 impact of underexposure more severe in the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the impact of underexposure more severe in 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([-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]: 0.5672,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 268:  27%|▎| 269/999 [03:15<
[Running Accuracy]: 0.5688,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 269:  27%|▎| 269/999 [03:15<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 impact of underexposure more severe 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 first image inferior to 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 inferior to 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 inferior to 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([-31.3281], 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]: 0.5688,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 269:  27%|▎| 270/999 [03:16<08:2
[Running Accuracy]: 0.5667,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 270:  27%|▎| 270/999 [03:16<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 clarity of the first image inferior to 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 details and textures in the second image?
A. Similar
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 details and textures in the second image?
A. Similar
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 details and textures in the second image?\nA. Similar\nB. Much blurrier\nC. Much 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([-31.2656], 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]: 0.5667,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 270:  27%|▎| 271/999 [03:17<09:4
[Running Accuracy]: 0.5683,[Response]: B.<|endoftext|>, [Correct Ans]: Much blurrier, , [Prog]: 271:  27%|▎| 271/999 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 details and textures in the second image?\nA. Similar\nB. Much blurrier\nC. Much 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 of the following is NOT included in the more severe distortion in the first image compared to the second image?
A. Low light
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:Which of the following is NOT included in the more severe distortion in the first image compared to the second image?
A. Low light
B. Noise
C. Blur
D. Underexposure
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following is NOT included in the more severe distortion in the first image compared to the second image?\nA. Low light\nB. Noise\nC. Blur\nD. Underexposure\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([-31.2031], 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])
 D.

[Running Accuracy]: 0.5683,[Response]: B.<|endoftext|>, [Correct Ans]: Much blurrier, , [Prog]: 271:  27%|▎| 272/999 [
[Running Accuracy]: 0.5662,[Response]: D.<|endoftext|>, [Correct Ans]: Blur, , [Prog]: 272:  27%|▎| 272/999 [03:18<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>Which of the following is NOT included in the more severe distortion in the first image compared to the second image?\nA. Low light\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 second image, how is the sharpness of the first image?
A. Slightly low
B. Slightly high
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 sharpness of the first image?
A. Slightly low
B. Slightly high
C. About the same
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. Slightly low\nB. Slightly high\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([-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]: 0.5662,[Response]: D.<|endoftext|>, [Correct Ans]: Blur, , [Prog]: 272:  27%|▎| 273/999 [03:19<10:
[Running Accuracy]: 0.5641,[Response]: A.<|endoftext|>, [Correct Ans]: Slightly high, , [Prog]: 273:  27%|▎| 273/999 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. Slightly low\nB. Slightly high\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 severely blurred?
A. Coffee in the second image
B. Building in the first image
C. Desk in the second image
D. Crowd 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 blurred?
A. Coffee in the second image
B. Building in the first image
C. Desk in the second image
D. Crowd in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which area is more severely blurred?\nA. Coffee in the second image\nB. Building in the first image\nC. Desk in the second image\nD. Crowd 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([-31.1094], 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]: 0.5641,[Response]: A.<|endoftext|>, [Correct Ans]: Slightly high, , [Prog]: 273:  27%|▎| 274/999 [
[Running Accuracy]: 0.5620,[Response]: A.<|endoftext|>, [Correct Ans]: Desk in the second 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 blurred?\nA. Coffee in the second image\nB. Building in the first image\nC. Desk in the second image\nD. Crowd 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>What is the apparent and more severe distortion in the second image compared to the first image?
A. Motion blur
B. Noise
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 is the apparent and more severe distortion in the second image compared to the first image?
A. Motion blur
B. Noise
C. Underexposure
D. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["What is the apparent and more severe distortion in the second image compared to the first image?\nA. Motion blur\nB. Noise\nC. Underexposure\nD. Overexposure\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([-30.8281], 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]: 0.5620,[Response]: A.<|endoftext|>, [Correct Ans]: Desk in the second image, , [Prog]: 274:  28%|▎
[Running Accuracy]: 0.5636,[Response]: B.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 275:  28%|▎| 275/999 [03:21<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 apparent and more severe distortion in the second image compared to the first image?\nA. Motion blur\nB. Noise\nC. Underexposure\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>Compared to the first image, how much is the second image affected by motion blur?
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 much is the second image affected by motion blur?
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 much is the second image affected by motion blur?\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([-30.7812], 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([-30.8594], 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]: 0.5636,[Response]: B.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 275:  28%|▎| 276/999 [03:21<10
[Running Accuracy]: 0.5652,[Response]: A.<|endoftext|>, [Correct Ans]: Much more severe, , [Prog]: 276:  28%|▎| 276/99
 {'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>Compared to the first image, how much is the second image affected by motion blur?\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 of the following is NOT a more serious issue in picture one than picture two?
A. motion blur
B. low light
C. underexposure
D. lens flare
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 issue in picture one than picture two?
A. motion blur
B. low light
C. underexposure
D. lens flare
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following is NOT a more serious issue in picture one than picture two?\nA. motion blur\nB. low light\nC. underexposure\nD. lens flare\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]: 0.5652,[Response]: A.<|endoftext|>, [Correct Ans]: Much more severe, , [Prog]: 276:  28%|▎| 277/99
[Running Accuracy]: 0.5632,[Response]: C.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 277:  28%|▎| 277/999 [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>Which of the following is NOT a more serious issue in picture one than picture two?\nA. motion blur\nB. low light\nC. underexposure\nD. lens flare\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([-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([-31.2812], 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]: 0.5632,[Response]: C.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 277:  28%|▎| 278/999 [03
[Running Accuracy]: 0.5647,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 278:  28%|▎| 278/999 [03:24<12:4
 {'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 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>Is the impact of motion blur on the first image significantly more severe than on 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 impact of motion blur on the first image significantly more severe than on the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the impact of motion blur on the first image significantly more severe than on 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([-30.6250], 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]: 0.5647,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 278:  28%|▎| 279/999 [03:25<12:3
[Running Accuracy]: 0.5663,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 279:  28%|▎| 279/999 [03:25<12:3
 {'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 impact of motion blur on the first image significantly more severe than on 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 do you rate the authenticity of the second image?
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 first image, how do you rate the authenticity of the second image?
A. Similar
B. More fake
C. More authentic
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how do you rate the authenticity of the second image?\nA. Similar\nB. More fake\nC. More authentic\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])
 B.

[Running Accuracy]: 0.5663,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 279:  28%|▎| 280/999 [03:26<12:3
[Running Accuracy]: 0.5643,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 280:  28%|▎| 280/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 do you rate the authenticity of the second image?\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>Is the lighting situation in the first image significantly better than that 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 lighting situation in the first image significantly better than that in the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the lighting situation in the first image significantly better than that in 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([-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]: 0.5643,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 280:  28%|▎| 281/999 
[Running Accuracy]: 0.5623,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 281:  28%|▎| 281/999 [03:27<11:5
 {'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 lighting situation in the first image significantly better than that in 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 of the following is not included in the more severe distortion in the second image compared to the first image?
A. Low illumination
B. Low definition
C. Motion blur
D. Unrealistic
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 more severe distortion in the second image compared to the first image?
A. Low illumination
B. Low definition
C. Motion blur
D. Unrealistic
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following is not included in the more severe distortion in the second image compared to the first image?\nA. Low illumination\nB. Low definition\nC. Motion blur\nD. Unrealistic\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([-31.0938], 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])
 D.

[Running Accuracy]: 0.5623,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 281:  28%|▎| 282/999 [03:28<11:2
[Running Accuracy]: 0.5638,[Response]: D.<|endoftext|>, [Correct Ans]: Unrealistic, , [Prog]: 282:  28%|▎| 282/999 [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>Which of the following is not included in the more severe distortion in the second image compared to the first image?\nA. Low illumination\nB. Low definition\nC. Motion blur\nD. Unrealistic\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 lighting in the first image not as good as 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 lighting in the first image not as good as in the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the lighting in the first image not as good as in 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([-30.8438], 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]: 0.5638,[Response]: D.<|endoftext|>, [Correct Ans]: Unrealistic, , [Prog]: 282:  28%|▎| 283/999 [03
[Running Accuracy]: 0.5654,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 283:  28%|▎| 283/999 [03:29<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 lighting in the first image not as good as 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>Compared to the first image, how is the sharpness of the second 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 to the first image, how is the sharpness of the second image?
A. Similar
B. Higher
C. Lower
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. Higher\nC. Lower\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([-31.0469], 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]: 0.5654,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 283:  28%|▎| 284/999 [03:29<10:5
[Running Accuracy]: 0.5669,[Response]: B.<|endoftext|>, [Correct Ans]: Higher, , [Prog]: 284:  28%|▎| 284/999 [03:29<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. Higher\nC. Lower\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 following, which one is NOT included in the more serious distortion of the first image compared to the second image?
A. Underexposure
B. Lens flare
C. Overexposure
D. Blur
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Among the following, which one is NOT included in the more serious distortion of the first image compared to the second image?
A. Underexposure
B. Lens flare
C. Overexposure
D. Blur
Answer with the option's letter from the given choices directly.

prompts: [["Among the following, which one is NOT included in the more serious distortion of the first image compared to the second image?\nA. Underexposure\nB. Lens flare\nC. Overexposure\nD. Blur\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])
 A.

[Running Accuracy]: 0.5669,[Response]: B.<|endoftext|>, [Correct Ans]: Higher, , [Prog]: 284:  29%|▎| 285/999 [03:30<1
[Running Accuracy]: 0.5684,[Response]: A.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 285:  29%|▎| 285/999 [
 {'prompt': "A chat between a curious user and an artificial 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 following, which one is NOT included in the more serious distortion of the first image compared to the second image?\nA. Underexposure\nB. Lens flare\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 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([-31.3438], 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([-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])
 B.

[Running Accuracy]: 0.5684,[Response]: A.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 285:  29%|▎| 286/999 [
[Running Accuracy]: 0.5664,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 286:  29%|▎| 286/999 [03:31<10:0
 {'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 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 impact of overexposure on the second image smaller than the impact of overexposure on 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 overexposure on the second image smaller than the impact of overexposure on the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the impact of overexposure on the second image smaller than the impact of overexposure on 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([-30.9219], 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]: 0.5664,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 286:  29%|▎| 287/999 [03:32<09:1
[Running Accuracy]: 0.5679,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 287:  29%|▎| 287/999 [03:32<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 impact of overexposure on the second image smaller than the impact of overexposure on 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 sharpness of the second image significantly higher 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 sharpness of the second image significantly higher than that of the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the sharpness of the second image significantly higher than that of the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.3125], 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([-31.1250], 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]: 0.5679,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 287:  29%|▎| 288/999 [03:33<09:34
[Running Accuracy]: 0.5694,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 288:  29%|▎| 288/999 [03:33<09:34
 {'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 sharpness of the second image significantly higher than that of 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>In the obvious more serious distortion of the first image than the second image, which of the following is not included?
A. Halo
B. Underexposure
C. Low light
D. Distortion
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:In the obvious more serious distortion of the first image than the second image, which of the following is not included?
A. Halo
B. Underexposure
C. Low light
D. Distortion
Answer with the option's letter from the given choices directly.

prompts: [["In the obvious more serious distortion of the first image than the second image, which of the following is not included?\nA. Halo\nB. Underexposure\nC. Low light\nD. Distortion\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([-31.2812], 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]: 0.5694,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 288:  29%|▎| 289/999 [03:33<08:55
[Running Accuracy]: 0.5675,[Response]: B.<|endoftext|>, [Correct Ans]: Distortion, , [Prog]: 289:  29%|▎| 289/999 [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>In the obvious more serious distortion of the first image than the second image, which of the following is not included?\nA. Halo\nB. Underexposure\nC. Low light\nD. Distortion\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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 situation of 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 focusing situation of 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 focusing situation of 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([-30.0781], 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([-31.4375], 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]: 0.5675,[Response]: B.<|endoftext|>, [Correct Ans]: Distortion, , [Prog]: 289:  29%|▎| 290/999 [03:
[Running Accuracy]: 0.5655,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 290:  29%|▎| 290/999 [03:34<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 focusing situation of 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>Has both images been affected by 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:Has both images been affected by overexposure?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Has both images been affected by overexposure?\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([-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]: 0.5655,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 290:  29%|▎| 291/999 [03:34<08:0
[Running Accuracy]: 0.5636,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 291:  29%|▎| 291/999 [03:34<08:03
 {'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>Has both images been affected by overexposure?\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 much better 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 exposure of the first image much better than that of the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the exposure of the first image much better than that of the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.7188], 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([-31.0781], 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]: 0.5636,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 291:  29%|▎| 292/999 [03:35<08:54
[Running Accuracy]: 0.5651,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 292:  29%|▎| 292/999 [03:35<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 exposure of the first image much better 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>Is the detailed texture of picture one clearer than picture two?
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 detailed texture of picture one clearer than picture two?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the detailed texture of picture one clearer than picture two?\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([-31.1250], 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]: 0.5651,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 292:  29%|▎| 293/999 [03:36<08:3
[Running Accuracy]: 0.5666,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 293:  29%|▎| 293/999 [03:36<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 detailed texture of picture one clearer than picture two?\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 exposure of the second image not as good as 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 exposure of the second image not as good as the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the exposure of the second image not as good as the first image?\nA. No\nB. Yes\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([-31.2969], 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]: 0.5666,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 293:  29%|▎| 294/999 [03:37<08:1
[Running Accuracy]: 0.5646,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 294:  29%|▎| 294/999 [03:37<08:15
 {'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 exposure of the second image not as good as 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. 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 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 detail texture of 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([-31.3281], 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])
 B.

[Running Accuracy]: 0.5646,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 294:  30%|▎| 295/999 [03:37<08:02
[Running Accuracy]: 0.5661,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 295:  30%|▎| 295/999 [03:37<08:02
 {'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 detail texture of 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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 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 texture detail of 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 texture detail of 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([-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([-31.0312], 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]: 0.5661,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 295:  30%|▎| 296/999 [03:38<07:52
[Running Accuracy]: 0.5642,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 296:  30%|▎| 296/999 [03:38<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 texture detail of 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>Which part below is most severely affected by overexposure?
A. Background of the second image
B. Person 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 below is most severely affected by overexposure?
A. Background of the second image
B. Person in the first 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. Background of the second image\nB. Person in the first image\nC. Ground in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2969], 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([-31.0312], 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]: 0.5642,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 296:  30%|▎| 297/999 [03:39<08:52
[Running Accuracy]: 0.5623,[Response]: B.<|endoftext|>, [Correct Ans]: Background of the second image, , [Prog]: 297: 
 {'prompt': "A chat between a curious user and an artificial 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 of the second image\nB. Person in the first 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>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([-31.0156], 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]: 0.5623,[Response]: B.<|endoftext|>, [Correct Ans]: Background of the second image, , [Prog]: 297: 
[Running Accuracy]: 0.5638,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 298:  30%|▎| 298/999 [03:40<09:20
 {'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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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 significantly 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 significantly 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 significantly 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([-31.6406], 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]: 0.5638,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 298:  30%|▎| 299/999 [03:40<08:50
[Running Accuracy]: 0.5652,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 299:  30%|▎| 299/999 [03:40<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 sharpness of the first image significantly 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>Which part is most affected by noise?
A. The ground in the first image
B. The cat in the first image
C. The doll 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 noise?
A. The ground in the first image
B. The cat in the first image
C. The doll in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most affected by noise?\nA. The ground in the first image\nB. The cat in the first image\nC. The doll 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([-30.3281], 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]: 0.5652,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 299:  30%|▎| 300/999 [03:41<08:2
[Running Accuracy]: 0.5633,[Response]: B.<|endoftext|>, [Correct Ans]: The doll in the second image, , [Prog]: 300:  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 is most affected by noise?\nA. The ground in the first image\nB. The cat in the first image\nC. The doll 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 relatively authentic?
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 authentic?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images relatively authentic?\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([-31.4219], 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]: 0.5633,[Response]: B.<|endoftext|>, [Correct Ans]: The doll in the second image, , [Prog]: 300:  3
[Running Accuracy]: 0.5648,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 301:  30%|▎| 301/999 [03:42<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>Are both of these images relatively authentic?\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 issue do these two images not have?
A. overexposure
B. lens flare
C. snowflake-like 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 do these two images not have?
A. overexposure
B. lens flare
C. snowflake-like noise
Answer with the option's letter from the given choices directly.

prompts: [["Which kind of distortion issue do these two images not have?\nA. overexposure\nB. lens flare\nC. snowflake-like noise\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])
 C.

[Running Accuracy]: 0.5648,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 301:  30%|▎| 302/999 [03:42<07:54
[Running Accuracy]: 0.5662,[Response]: C.<|endoftext|>, [Correct Ans]: snowflake-like noise, , [Prog]: 302:  30%|▎| 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>Which kind of distortion issue do these two images not have?\nA. overexposure\nB. lens flare\nC. snowflake-like 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. Almost 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. Almost 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. Almost the same\nB. More blurry\nC. Clearer\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([-30.6719], 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]: 0.5662,[Response]: C.<|endoftext|>, [Correct Ans]: snowflake-like noise, , [Prog]: 302:  30%|▎| 30
[Running Accuracy]: 0.5677,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 303:  30%|▎| 303/999 [03:43<
 {'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>Compared to the first image, how is the clarity of the second image?\nA. Almost 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>How does the sharpness of the second image compare to 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:How does the sharpness of the second image compare to the first image?
A. More blurry
B. About the same
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. More blurry\nB. About the same\nC. Clearer\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([-30.6406], 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]: 0.5677,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 303:  30%|▎| 304/999 [03:44<
[Running Accuracy]: 0.5691,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 304:  30%|▎| 304/999 [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 sharpness of the second image compare to 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 below is most severely affected by overexposure?
A. The sky part of the second image
B. The sandy area of the first image
C. The bird 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 part of the second image
B. The sandy area of the first image
C. The bird 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 part of the second image\nB. The sandy area of the first image\nC. The bird 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([-30.9688], 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]: 0.5691,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 304:  31%|▎| 305/999 [03
[Running Accuracy]: 0.5672,[Response]: B.<|endoftext|>, [Correct Ans]: The sky part of the second image, , [Prog]: 305
 {'prompt': "A chat between a curious user and an artificial 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 part of the second image\nB. The sandy area of the first image\nC. The bird 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 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([-31.3281], 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])
 B.

[Running Accuracy]: 0.5672,[Response]: B.<|endoftext|>, [Correct Ans]: The sky part of the second image, , [Prog]: 305
[Running Accuracy]: 0.5654,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 306:  31%|▎| 306/999 [03:45<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>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>Are both of these images not well-illuminated?
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 well-illuminated?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images not well-illuminated?\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([-30.7812], 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]: 0.5654,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 306:  31%|▎| 307/999 [03:46<08:0
[Running Accuracy]: 0.5668,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 307:  31%|▎| 307/999 [03:46<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 well-illuminated?\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 sharpness of the second image compare to the first one?
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:How does the sharpness of the second image compare to the first one?
A. Sharper
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["How does the sharpness of the second image compare to the first one?\nA. Sharper\nB. Blurrier\nC. About the same\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([-31.3281], 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]: 0.5668,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 307:  31%|▎| 308/999 [03:47<07:5
[Running Accuracy]: 0.5649,[Response]: C.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 308:  31%|▎| 308/999 [03:47<
 {'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>How does the sharpness of the second image compare to the first one?\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>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([-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]: 0.5649,[Response]: C.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 308:  31%|▎| 309/999 [03:47<
[Running Accuracy]: 0.5631,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 309:  31%|▎| 309/999 [03:47<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>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>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([-31.3594], 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([-31.1719], 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]: 0.5631,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 309:  31%|▎| 310/999 [03:48<07:4
[Running Accuracy]: 0.5645,[Response]: A.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 310:  31%|▎| 310/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 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([-31.3906], 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([-31.3125], 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]: 0.5645,[Response]: A.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 310:  31%|▎| 311/999 
[Running Accuracy]: 0.5659,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 311:  31%|▎| 311/999 [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. 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>Is the illumination sufficient in both of 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 both of these two 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 two 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([-31.5000], 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]: 0.5659,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 311:  31%|▎| 312/999 [03
[Running Accuracy]: 0.5673,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 312:  31%|▎| 312/999 [03:49<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 illumination sufficient in both of 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 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([-31.2969], 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])
 B.

[Running Accuracy]: 0.5673,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 312:  31%|▎| 313/999 [03:50<07:3
[Running Accuracy]: 0.5687,[Response]: B.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 313:  31%|▎| 313/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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([-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([-31.4219], 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]: 0.5687,[Response]: B.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 313:  31%|▎| 314/999 
[Running Accuracy]: 0.5669,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 314:  31%|▎| 314/999 [03:51<07:31
 {'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 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 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([-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([-30.7656], 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]: 0.5669,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 314:  32%|▎| 315/999 [03:51<07:32
[Running Accuracy]: 0.5651,[Response]: B.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 315:  32%|▎| 315/999 [03:51<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. Better\nB. 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>Are there certain overexposure 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 certain overexposure issues in both of these images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are there certain overexposure issues in both of these 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([-31.1875], 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]: 0.5651,[Response]: B.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 315:  32%|▎| 316/999 [03:52<0
[Running Accuracy]: 0.5665,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 316:  32%|▎| 316/999 [03:52<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>Are there certain overexposure 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 part has the richest texture details?
A. Ground in the second image
B. Cat's head in the first image
C. 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 has the richest texture details?
A. Ground in the second image
B. Cat's head in the first image
C. Tabletop in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the richest texture details?\nA. Ground in the second image\nB. Cat's head in the first image\nC. Tabletop in the first image\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([-31.2969], 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]: 0.5665,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 316:  32%|▎| 317/999 [03:53<07:2
[Running Accuracy]: 0.5678,[Response]: B.<|endoftext|>, [Correct Ans]: Cat's head in the first image, , [Prog]: 317:  
 {'prompt': "A chat between a curious user and an artificial 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 second image\nB. Cat's head in the first image\nC. 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 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([-30.4062], 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([-30.7969], 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]: 0.5678,[Response]: B.<|endoftext|>, [Correct Ans]: Cat's head in the first image, , [Prog]: 317:  
[Running Accuracy]: 0.5660,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 318:  32%|▎| 318/999 [03:53<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 blurrier 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 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([-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([-31.2031], 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]: 0.5660,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 318:  32%|▎| 319/999 [03:54<07:3
[Running Accuracy]: 0.5643,[Response]: C.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 319:  32%|▎| 319/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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': '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. 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([-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([-31.0781], 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]: 0.5643,[Response]: C.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 319:  32%|▎| 320/999
[Running Accuracy]: 0.5625,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 320:  32%|▎| 320/999 [03:55<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 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 part is most severely affected by motion blur?
A. the person in the first image
B. the trees 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 is most severely affected by motion blur?
A. the person in the first image
B. the trees 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 is most severely affected by motion blur?\nA. the person in the first image\nB. the trees 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.], device='cuda:0', dtype=torch.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]: 0.5625,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 320:  32%|▎| 321/999 [03:56<08:1
[Running Accuracy]: 0.5607,[Response]: A.<|endoftext|>, [Correct Ans]: the trees in the second image, , [Prog]: 321:  
 {'prompt': "A chat between a curious user and an artificial 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 person in the first image\nB. the trees 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 is the sharpness of the second image?
A. About the same
B. More blurry
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. About the same
B. More blurry
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. About the same\nB. More blurry\nC. Sharper\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([-31.2500], 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]: 0.5607,[Response]: A.<|endoftext|>, [Correct Ans]: the trees in the second image, , [Prog]: 321:  
[Running Accuracy]: 0.5590,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 322:  32%|▎| 322/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. More blurry\nC. Sharper\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([-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([-30.9062], 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]: 0.5590,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 322:  32%|▎| 323/999 
[Running Accuracy]: 0.5573,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 323:  32%|▎| 323/999 [03:57<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 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 less sharp 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 sharp 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 sharp 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([-30.8750], 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]: 0.5573,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 323:  32%|▎| 324/999 [03:58<07:4
[Running Accuracy]: 0.5586,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 324:  32%|▎| 324/999 [03:58<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 first image less sharp 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 has richer texture details?
A. The characters in the second image
B. The dog 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 has richer texture details?
A. The characters in the second image
B. The dog 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 has richer texture details?\nA. The characters in the second image\nB. The dog 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([-31.2812], 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]: 0.5586,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 324:  33%|▎| 325/999 [03:58<07:4
[Running Accuracy]: 0.5569,[Response]: A.<|endoftext|>, [Correct Ans]: The dog in the first image, , [Prog]: 325:  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 richer texture details?\nA. The characters in the second image\nB. The dog 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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 much higher 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 sharpness of the first image much higher than 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 much higher 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([-31.5469], 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]: 0.5569,[Response]: A.<|endoftext|>, [Correct Ans]: The dog in the first image, , [Prog]: 325:  33%
[Running Accuracy]: 0.5552,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 326:  33%|▎| 326/999 [03:59<07:40
 {'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 sharpness of the first image much higher 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 authenticity of the second image compare to the first 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:How does the authenticity of the second image compare to the first image?
A. Less authentic
B. About the same
C. More authentic
Answer with the option's letter from the given choices directly.

prompts: [["How does the authenticity of the second image compare to the first 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([-31.3750], 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([-30.7969], 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]: 0.5552,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 326:  33%|▎| 327/999 [04:00<07:40
[Running Accuracy]: 0.5535,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 327:  33%|▎| 327/999 
 {'prompt': "A chat between a curious user and an artificial 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 authenticity of the second image compare to the first 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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 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 authenticity 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 authenticity 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([-31.1406], 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([-31.0625], 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]: 0.5535,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 327:  33%|▎| 328/999 
[Running Accuracy]: 0.5518,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 328:  33%|▎| 328/999 [04:00<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 authenticity 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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([-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([-30.4375], 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]: 0.5518,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 328:  33%|▎| 329/999 [04:01<08:3
[Running Accuracy]: 0.5502,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 329:  33%|▎| 329/999 [04:01<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 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 underexposed?
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 underexposed?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images underexposed?\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([-31.0469], 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]: 0.5502,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 329:  33%|▎| 330/999 [04:02<08:5
[Running Accuracy]: 0.5515,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 330:  33%|▎| 330/999 [04:02<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>Are both of these images underexposed?\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 severely affected by motion blur?
A. Background of the second image
B. People in the second image
C. Left side vehicle 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 severely affected by motion blur?
A. Background of the second image
B. People in the second image
C. Left side vehicle of 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. Background of the second image\nB. People in the second image\nC. Left side vehicle of 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([-31.4219], 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]: 0.5515,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 330:  33%|▎| 331/999 [04:03<08:2
[Running Accuracy]: 0.5498,[Response]: B.<|endoftext|>, [Correct Ans]: Left side vehicle 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 is most severely affected by motion blur?\nA. Background of the second image\nB. People in the second image\nC. Left side vehicle 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>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([-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([-31.2188], 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]: 0.5498,[Response]: B.<|endoftext|>, [Correct Ans]: Left side vehicle of the first image, , [Prog]:
[Running Accuracy]: 0.5512,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 332:  33%|▎| 332/999 [04:04<08:23
 {'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 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 first image
B. Ground of 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 noise?
A. Background of the first image
B. Ground of 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 noise?\nA. Background of the first image\nB. Ground of the first image\nC. Person 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([-30.8125], 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])
 C.

[Running Accuracy]: 0.5512,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 332:  33%|▎| 333/999 [04:04<07:52
[Running Accuracy]: 0.5526,[Response]: C.<|endoftext|>, [Correct Ans]: Person in the second 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 below is most severely affected by noise?\nA. Background of the first image\nB. Ground of 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>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([-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]: 0.5526,[Response]: C.<|endoftext|>, [Correct Ans]: Person in the second image, , [Prog]: 333:  33%
[Running Accuracy]: 0.5509,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 334:  33%|▎| 334/999 [04:05<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 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 sharpness of the second image 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 sharpness of the second image 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 sharpness of the second image 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([-31.3281], 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]: 0.5509,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 334:  34%|▎| 335/999 [04:05<07:2
[Running Accuracy]: 0.5522,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 335:  34%|▎| 335/999 [04:05<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 second image 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>Which part below is most severely affected by overexposure?
A. The horse in the first image
B. The sky 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 horse in the first image
B. The sky 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 horse in the first image\nB. The sky 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([-30.8281], 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([-30.2812], 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]: 0.5522,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 335:  34%|▎| 336/999 [04:06<07:1
[Running Accuracy]: 0.5536,[Response]: B.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 336:  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>Which part below is most severely affected by overexposure?\nA. The horse in the first image\nB. The sky 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 sharpness of the first image higher 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 sharpness of the first image higher than 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 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([-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]: 0.5536,[Response]: B.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 336:  34
[Running Accuracy]: 0.5549,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 337:  34%|▎| 337/999 [04:07<07:09
 {'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 sharpness of the first image higher 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 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([-31.1094], 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([-30.4688], 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]: 0.5549,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 337:  34%|▎| 338/999 [04:07<07:09
[Running Accuracy]: 0.5533,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 338:  34%|▎| 338/999 [04:07<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 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>Are both of these images quite 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 quite blurry?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images quite blurry?\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])
 B.

[Running Accuracy]: 0.5533,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 338:  34%|▎| 339/999 [04:08<07:1
[Running Accuracy]: 0.5546,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 339:  34%|▎| 339/999 [04:08<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 quite 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>Is the sharpness of these two images relatively low?
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 these two images relatively low?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the sharpness of these two images relatively low?\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([-31.2656], 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]: 0.5546,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 339:  34%|▎| 340/999 [04:09<07:1
[Running Accuracy]: 0.5559,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 340:  34%|▎| 340/999 [04:09<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>Is the sharpness of these two images relatively low?\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([-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([-30.9531], 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]: 0.5559,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 340:  34%|▎| 341/999 [04:09<07:13
[Running Accuracy]: 0.5572,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 341:  34%|▎| 341/999 [04:09<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 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 most abundant texture details?
A. The character's cheeks in the second image
B. The background in the first image
C. The eyes of the character 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 abundant texture details?
A. The character's cheeks in the second image
B. The background in the first image
C. The eyes of the character in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the most abundant texture details?\nA. The character's cheeks in the second image\nB. The background in the first image\nC. The eyes of the character 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([-31.0312], 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]: 0.5572,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 341:  34%|▎| 342/999 [04:10<07:12
[Running Accuracy]: 0.5585,[Response]: C.<|endoftext|>, [Correct Ans]: The eyes of the character 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 most abundant texture details?\nA. The character's cheeks in the second image\nB. The background in the first image\nC. The eyes of the character 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 sharp?
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 sharp?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images sharp?\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([-31.0156], 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]: 0.5585,[Response]: C.<|endoftext|>, [Correct Ans]: The eyes of the character in the second image, 
[Running Accuracy]: 0.5598,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 343:  34%|▎| 343/999 [04:11<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>Are both of these images sharp?\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([-31.2188], 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([-30.7656], 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]: 0.5598,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 343:  34%|▎| 344/999 [04:11<07:02
[Running Accuracy]: 0.5581,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 344:  34%|▎| 344/999 [04:11<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 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>Which part below is most severely affected by overexposure?
A. The ground in the first image
B. The street lamp in the second 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 below is most severely affected by overexposure?
A. The ground in the first image
B. The street lamp in the second image
C. The 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 overexposure?\nA. The ground in the first image\nB. The street lamp in the second image\nC. The trees 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([-30.8906], 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]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 344:  35%|▎| 345/999 [04:12<07:5
[Running Accuracy]: 0.5594,[Response]: B.<|endoftext|>, [Correct Ans]: The street lamp 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 ground in the first image\nB. The street lamp in the second image\nC. The 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>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([-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([-30.8594], 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]: 0.5594,[Response]: B.<|endoftext|>, [Correct Ans]: The street lamp in the second image, , [Prog]: 
[Running Accuracy]: 0.5607,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 346:  35%|▎| 346/999 [04:13<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 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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. 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 differ?
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 differ?\nA. Similar\nB. Less realistic\nC. More realistic\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([-30.5469], 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]: 0.5607,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 346:  35%|▎| 347/999 [04:14<07:3
[Running Accuracy]: 0.5620,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 347:  35%|▎| 347/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. 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 real is 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 real is 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 real is 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([-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([-31.2344], 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]: 0.5620,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 347:  35%|▎| 348/999 
[Running Accuracy]: 0.5603,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 348:  35%|▎| 348/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 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>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([-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([-31.2188], 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]: 0.5603,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 348:  35%|▎| 349/999 
[Running Accuracy]: 0.5587,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 349:  35%|▎| 349/999 [04:15<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 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>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([-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([-30.9219], 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]: 0.5587,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 349:  35%|▎| 350/999 [04:15<07:0
[Running Accuracy]: 0.5600,[Response]: C.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 350:  35%|▎| 350/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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>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([-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])
 B.

[Running Accuracy]: 0.5600,[Response]: C.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 350:  35%|▎| 351/999
[Running Accuracy]: 0.5584,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 351:  35%|▎| 351/999 [04:16<07:02
 {'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 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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. 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 realism 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 realism 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([-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([-31.1875], 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]: 0.5584,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 351:  35%|▎| 352/999 [04:17<07:03
[Running Accuracy]: 0.5597,[Response]: A.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 352:  35%|▎| 352/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. 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 severely affected by motion blur?
A. Ground of the first image
B. Insect of the second image
C. Leaves 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 motion blur?
A. Ground of the first image
B. Insect of the second image
C. Leaves of 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. Ground of the first image\nB. Insect of the second image\nC. Leaves of 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([-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]: 0.5597,[Response]: A.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 352:  35%|▎| 353/999 
[Running Accuracy]: 0.5581,[Response]: B.<|endoftext|>, [Correct Ans]: Ground of the first image, , [Prog]: 353:  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>Which part below is most severely affected by motion blur?\nA. Ground of the first image\nB. Insect of the second image\nC. Leaves 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>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([-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([-30.9531], 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]: 0.5581,[Response]: B.<|endoftext|>, [Correct Ans]: Ground of the first image, , [Prog]: 353:  35%|
[Running Accuracy]: 0.5565,[Response]: B.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 354:  35%|▎| 354/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 has the richest texture details?
A. Child in the first image
B. Background in the second image
C. 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 has the richest texture details?
A. Child in the first image
B. Background in the second image
C. Tabletop in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the richest texture details?\nA. Child in the first image\nB. Background in the second image\nC. Tabletop 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([-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]: 0.5565,[Response]: B.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 354:  36%|▎| 355/999
[Running Accuracy]: 0.5549,[Response]: B.<|endoftext|>, [Correct Ans]: Child in the first image, , [Prog]: 355:  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 has the richest texture details?\nA. Child in the first image\nB. Background in the second image\nC. 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>Compared to the first image, how is the illumination in 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 in 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 in 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([-30.2500], 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])
 C.

[Running Accuracy]: 0.5549,[Response]: B.<|endoftext|>, [Correct Ans]: Child in the first image, , [Prog]: 355:  36%|▎
[Running Accuracy]: 0.5562,[Response]: C.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 356:  36%|▎| 356/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. 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. 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([-31.0312], 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([-30.9219], 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]: 0.5562,[Response]: C.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 356:  36%|▎| 357/999
[Running Accuracy]: 0.5574,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 357:  36%|▎| 357/999 [04:20
 {'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. Clearer\nB. Blurrier\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([-31.5469], 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([-31.3750], 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]: 0.5574,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 357:  36%|▎| 358/999 [04:21
[Running Accuracy]: 0.5559,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 358:  36%|▎| 358/999 [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. 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 clarity 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 clarity 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 clarity 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([-31.2500], 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([-30.9062], 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]: 0.5559,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 358:  36%|▎| 359/999 [04
[Running Accuracy]: 0.5543,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 359:  36%|▎| 359/999 [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. Similar\nB. Clearer\nC. More blurry\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 defocus?
A. The background of the first image
B. The person in the second image
C. The rocks 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 defocus?
A. The background of the first image
B. The person in the second image
C. The rocks in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by defocus?\nA. The background of the first image\nB. The person in the second image\nC. The rocks 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([-31.1719], 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]: 0.5543,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 359:  36%|▎| 360/999 [04
[Running Accuracy]: 0.5528,[Response]: B.<|endoftext|>, [Correct Ans]: The background of the first image, , [Prog]: 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 defocus?\nA. The background of the first image\nB. The person in the second image\nC. The rocks 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. Figures in the second image
B. Sky in the second image
C. Sphinx 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. Figures in the second image
B. Sky in the second image
C. Sphinx 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. Figures in the second image\nB. Sky in the second image\nC. Sphinx in 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([-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]: 0.5528,[Response]: B.<|endoftext|>, [Correct Ans]: The background of the first image, , [Prog]: 36
[Running Accuracy]: 0.5540,[Response]: B.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 361:  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. Figures in the second image\nB. Sky in the second image\nC. Sphinx 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's 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's 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's 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([-31.2500], 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]: 0.5540,[Response]: B.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 361:  36%|▎|
[Running Accuracy]: 0.5552,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 362:  36%|▎| 362/999 [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's 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 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([-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([-31.3594], 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]: 0.5552,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 362:  36%|▎| 363/999 [04
[Running Accuracy]: 0.5565,[Response]: C.<|endoftext|>, [Correct Ans]: Less Rich, , [Prog]: 363:  36%|▎| 363/999 [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>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>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([-31.4062], 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([-31.3438], 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]: 0.5565,[Response]: C.<|endoftext|>, [Correct Ans]: Less Rich, , [Prog]: 363:  36%|▎| 364/999 [04:2
[Running Accuracy]: 0.5549,[Response]: C.<|endoftext|>, [Correct Ans]: less sufficient, , [Prog]: 364:  36%|▎| 364/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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>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([-30.9062], 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([-30.8125], 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]: 0.5549,[Response]: C.<|endoftext|>, [Correct Ans]: less sufficient, , [Prog]: 364:  37%|▎| 365/999
[Running Accuracy]: 0.5534,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 365:  37%|▎| 365/999 [04:26<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 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 texture detail 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 texture detail 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 texture detail 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([-31.2656], 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])
 C.

[Running Accuracy]: 0.5534,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 365:  37%|▎| 366/999 [04:27<07:0
[Running Accuracy]: 0.5519,[Response]: C.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 366:  37%|▎| 366/999 [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>Compared to the first image, how is the texture detail 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>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([-31.3594], 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([-31.4688], 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]: 0.5519,[Response]: C.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 366:  37%|▎| 367/999 [04:2
[Running Accuracy]: 0.5531,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 367:  37%|▎| 367/999 [04:27<07:00
 {'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 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 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([-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]: 0.5531,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 367:  37%|▎| 368/999 [04:28<07:10
[Running Accuracy]: 0.5543,[Response]: B.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 368:  37%|▎| 368/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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>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([-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([-29.6406], 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]: 0.5543,[Response]: B.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 368:  37%|▎| 369/999 
[Running Accuracy]: 0.5556,[Response]: B.<|endoftext|>, [Correct Ans]: more monotonous, , [Prog]: 369:  37%|▎| 369/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 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([-31.5312], 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([-31.3281], 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]: 0.5556,[Response]: B.<|endoftext|>, [Correct Ans]: more monotonous, , [Prog]: 369:  37%|▎| 370/999
[Running Accuracy]: 0.5568,[Response]: A.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 370:  37%|▎| 370/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 one?
A. Similar
B. Sharper
C. More blurry
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 one?
A. Similar
B. Sharper
C. More blurry
Answer with the option's letter from the given choices directly.

prompts: [["How does the sharpness of the second image compare to the first one?\nA. Similar\nB. Sharper\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([-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]: 0.5568,[Response]: A.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 370:  37%|▎| 371/999 
[Running Accuracy]: 0.5580,[Response]: C.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 371:  37%|▎| 371/999 [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>How does the sharpness of the second image compare to the first one?\nA. Similar\nB. Sharper\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>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([-31.4688], 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([-30.8594], 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]: 0.5580,[Response]: C.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 371:  37%|▎| 372/999 [04
[Running Accuracy]: 0.5565,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 372:  37%|▎| 372/999 [04:31<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 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 railing in the first image
B. The ground in the second image
C. The person's hair 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 railing in the first image
B. The ground in the second image
C. The person's hair 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 railing in the first image\nB. The ground in the second image\nC. The person's hair in the first image\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([-31.5469], 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]: 0.5565,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 372:  37%|▎| 373/999 [04:32<08:1
[Running Accuracy]: 0.5576,[Response]: C.<|endoftext|>, [Correct Ans]: The person's hair 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 affected by overexposure?\nA. The railing in the first image\nB. The ground in the second image\nC. The person's hair 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 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([-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]: 0.5576,[Response]: C.<|endoftext|>, [Correct Ans]: The person's hair in the first image, , [Prog]:
[Running Accuracy]: 0.5588,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 374:  37%|▎| 374/999 [04:32<07:34
 {'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 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 is most affected by motion blur?
A. Background of the second image
B. People in the second image
C. The person's arm 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. Background of the second image
B. People in the second image
C. The person's arm 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. Background of the second image\nB. People in the second image\nC. The person's arm 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([-30.0469], 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]: 0.5588,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 374:  38%|▍| 375/999 [04:33<07:19
[Running Accuracy]: 0.5573,[Response]: B.<|endoftext|>, [Correct Ans]: The person's arm 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 is most affected by motion blur?\nA. Background of the second image\nB. People in the second image\nC. The person's arm 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 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([-31.2500], 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([-31.3125], 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]: 0.5573,[Response]: B.<|endoftext|>, [Correct Ans]: The person's arm in the first image, , [Prog]: 
[Running Accuracy]: 0.5585,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 376:  38%|▍| 376/999 [04:34<07:03
 {'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 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. Sky in the first image
B. People 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 affected by overexposure?
A. Sky in the first image
B. People 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 affected by overexposure?\nA. Sky in the first image\nB. People in the first image\nC. People in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9688], 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([-31.4375], 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]: 0.5585,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 376:  38%|▍| 377/999 [04:34<06:58
[Running Accuracy]: 0.5597,[Response]: A.<|endoftext|>, [Correct Ans]: Sky in the first 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 below is most affected by overexposure?\nA. Sky in the first image\nB. People 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>Which part below is most affected by motion blur?
A. Characters in the first image
B. Background in the second image
C. 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 below is most affected by motion blur?
A. Characters in the first image
B. Background in the second image
C. Characters 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. Characters in the first image\nB. Background in the second image\nC. Characters 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([-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]: 0.5597,[Response]: A.<|endoftext|>, [Correct Ans]: Sky in the first image, , [Prog]: 377:  38%|▍| 
[Running Accuracy]: 0.5582,[Response]: A.<|endoftext|>, [Correct Ans]: Background in the second image, , [Prog]: 378: 
 {'prompt': "A chat between a curious user and an artificial 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. Characters in the first image\nB. Background in the second image\nC. 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>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([-30.7500], 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([-31.3125], 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]: 0.5582,[Response]: A.<|endoftext|>, [Correct Ans]: Background in the second image, , [Prog]: 378: 
[Running Accuracy]: 0.5594,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 379:  38%|▍| 379/999 [04:36<07:09
 {'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 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 color of the first image more 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 more 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 more 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([-31.3750], 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]: 0.5594,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 379:  38%|▍| 380/999 [04:36<07:01
[Running Accuracy]: 0.5579,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 380:  38%|▍| 380/999 [04:36<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 more 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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. Figures in the first image
B. The ground in the first image
C. The top part 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. Figures in the first image
B. The ground in the first image
C. The top part 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. Figures in the first image\nB. The ground in the first image\nC. The top part of 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([-30.9844], 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]: 0.5579,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 380:  38%|▍| 381/999 [04:37<06:5
[Running Accuracy]: 0.5591,[Response]: C.<|endoftext|>, [Correct Ans]: The top part of the second image, , [Prog]: 381
 {'prompt': "A chat between a curious user and an artificial 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. Figures in the first image\nB. The ground in the first image\nC. The top part 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>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([-31.1562], 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([-31.2969], 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]: 0.5591,[Response]: C.<|endoftext|>, [Correct Ans]: The top part of the second image, , [Prog]: 381
[Running Accuracy]: 0.5576,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 382:  38%|▍| 382/999 [04:38<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 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([-31.2188], 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([-31.0938], 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]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 382:  38%|▍| 383/999 [04:38<06:4
[Running Accuracy]: 0.5561,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 383:  38%|▍| 383/999 [04:38<06:49
 {'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. 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. Subjects in the second image
B. Subjects in the first 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 below is most affected by motion blur?
A. Subjects in the second image
B. Subjects in the first image
C. Background 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. Subjects in the second image\nB. Subjects in the first image\nC. Background 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([-31.0781], 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]: 0.5561,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 383:  38%|▍| 384/999 [04:39<06:55
[Running Accuracy]: 0.5573,[Response]: A.<|endoftext|>, [Correct Ans]: Subjects in the second image, , [Prog]: 384:  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. Subjects in the second image\nB. Subjects in the first image\nC. 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 affected by motion blur?
A. Background of the second image
B. Person in the first image
C. Cyclist 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 second image
B. Person in the first image
C. Cyclist 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 second image\nB. Person in the first image\nC. Cyclist 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([-30.4688], 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]: 0.5573,[Response]: A.<|endoftext|>, [Correct Ans]: Subjects in the second image, , [Prog]: 384:  3
[Running Accuracy]: 0.5584,[Response]: A.<|endoftext|>, [Correct Ans]: Background of 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 motion blur?\nA. Background of the second image\nB. Person in the first image\nC. Cyclist 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 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([-31.2812], 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([-31.0625], 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]: 0.5584,[Response]: A.<|endoftext|>, [Correct Ans]: Background of the second image, , [Prog]: 385: 
[Running Accuracy]: 0.5596,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 386:  39%|▍| 386/999 [04:40<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>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 there overexposure 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 overexposure issues in both of these images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are there overexposure issues in both of these images?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.1875], 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])
 B.

[Running Accuracy]: 0.5596,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 386:  39%|▍| 387/999 [04:41<08:2
[Running Accuracy]: 0.5607,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 387:  39%|▍| 387/999 [04:41<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>Are there overexposure 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 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([-31.1875], 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])
 B.

[Running Accuracy]: 0.5607,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 387:  39%|▍| 388/999 [04:42<07:4
[Running Accuracy]: 0.5593,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 388:  39%|▍| 388/999 [04:42<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 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 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 of these images not very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images not very realistic?\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([-31.1875], 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]: 0.5593,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 388:  39%|▍| 389/999 [04:43<07:2
[Running Accuracy]: 0.5604,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 389:  39%|▍| 389/999 [04:43<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>Are both of these 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>Which part below is most affected by overexposure?
A. The ground in the first image
B. The fish in the second image
C. The right wall 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 ground in the first image
B. The fish in the second image
C. The right wall 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 ground in the first image\nB. The fish in the second image\nC. The right wall in the first 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([-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]: 0.5604,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 389:  39%|▍| 390/999 [04:43<07:2
[Running Accuracy]: 0.5590,[Response]: A.<|endoftext|>, [Correct Ans]: The right wall in the first image, , [Prog]: 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 below is most affected by overexposure?\nA. The ground in the first image\nB. The fish in the second image\nC. The right wall 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 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([-30.8281], 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([-31.6406], 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]: 0.5590,[Response]: A.<|endoftext|>, [Correct Ans]: The right wall in the first image, , [Prog]: 39
[Running Accuracy]: 0.5601,[Response]: C.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 391:  39%|▍| 391/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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>Compared to the first image, how is the clarity of the second image?
A. Clearer
B. Less clear
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. Less clear
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. Less clear\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([-31.1562], 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]: 0.5601,[Response]: C.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 391:  39%|▍| 392/999
[Running Accuracy]: 0.5612,[Response]: B.<|endoftext|>, [Correct Ans]: Less clear, , [Prog]: 392:  39%|▍| 392/999 [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. Less clear\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 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([-31.1094], 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([-31.0781], 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]: 0.5612,[Response]: B.<|endoftext|>, [Correct Ans]: Less clear, , [Prog]: 392:  39%|▍| 393/999 [04:
[Running Accuracy]: 0.5623,[Response]: C.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 393:  39%|▍| 393/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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': '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 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([-30.9219], 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]: 0.5623,[Response]: C.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 393:  39%|▍| 394/999 
[Running Accuracy]: 0.5609,[Response]: B.<|endoftext|>, [Correct Ans]: More real, , [Prog]: 394:  39%|▍| 394/999 [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>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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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([-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]: 0.5609,[Response]: B.<|endoftext|>, [Correct Ans]: More real, , [Prog]: 394:  40%|▍| 395/999 [04:4
[Running Accuracy]: 0.5620,[Response]: C.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 395:  40%|▍| 395/999 [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': '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 ground in the first image
B. The vehicle 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 first image
B. The vehicle 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 first image\nB. The vehicle 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([-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]: 0.5620,[Response]: C.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 395:  40%|▍| 396/999 [04
[Running Accuracy]: 0.5606,[Response]: C.<|endoftext|>, [Correct Ans]: The vehicle in the second image, , [Prog]: 396:
 {'prompt': "A chat between a curious user and an artificial 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 first image\nB. The vehicle 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': '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 person in the first image more realistic than the person 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 person in the first image more realistic than the person in the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the person in the first image more realistic than the person in the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
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])
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]: 0.5606,[Response]: C.<|endoftext|>, [Correct Ans]: The vehicle in the second image, , [Prog]: 396:
[Running Accuracy]: 0.5592,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 397:  40%|▍| 397/999 [04:49<08:33
 {'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 person in the first image more realistic than the person 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>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([-31.2656], 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([-30.5781], 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]: 0.5592,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 397:  40%|▍| 398/999 [04:50<08:23
[Running Accuracy]: 0.5603,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 398:  40%|▍| 398/999 [04:50<08:23
 {'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 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 does the realism 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 realism 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 realism 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([-31.2344], 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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5603,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 398:  40%|▍| 399/999 [04:51<08:01
[Running Accuracy]: 0.5589,[Response]: A.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 399:  40%|▍| 399/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. 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>How does the clarity of the second image compare to the first 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:How does the clarity of the second image compare to the first image?
A. Similar
B. More blurry
C. Clearer
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. More blurry\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([-31.0469], 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]: 0.5589,[Response]: A.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 399:  40%|▍| 400/999 
[Running Accuracy]: 0.5600,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 400:  40%|▍| 400/999 [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>How does the clarity of the second image compare to the first 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>How is the lighting of the second image compared to the first image?
A. Less Adequate
B. More Adequate
C. Similar
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How is the lighting of the second image compared to the first image?
A. Less Adequate
B. More Adequate
C. Similar
Answer with the option's letter from the given choices directly.

prompts: [["How is the lighting of the second image compared to the first image?\nA. Less Adequate\nB. More Adequate\nC. Similar\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([-31.3906], 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]: 0.5600,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 400:  40%|▍| 401/999 [04
[Running Accuracy]: 0.5611,[Response]: B.<|endoftext|>, [Correct Ans]: More Adequate, , [Prog]: 401:  40%|▍| 401/999 [
 {'prompt': "A chat between a curious user and an artificial 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 lighting of the second image compared to the first image?\nA. Less Adequate\nB. More Adequate\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 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([-31.0781], 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([-30.7812], 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]: 0.5611,[Response]: B.<|endoftext|>, [Correct Ans]: More Adequate, , [Prog]: 401:  40%|▍| 402/999 [
[Running Accuracy]: 0.5597,[Response]: B.<|endoftext|>, [Correct Ans]: more sufficient, , [Prog]: 402:  40%|▍| 402/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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([-31.2500], 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([-30.3594], 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]: 0.5597,[Response]: B.<|endoftext|>, [Correct Ans]: more sufficient, , [Prog]: 402:  40%|▍| 403/999
[Running Accuracy]: 0.5608,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 403:  40%|▍| 403/999 [04:54<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 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>Compared to the first image, how authentic is 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 authentic is 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 authentic is 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([-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([-31.2188], 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]: 0.5608,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 403:  40%|▍| 404/999 [04:55<07:2
[Running Accuracy]: 0.5594,[Response]: B.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 404:  40%|▍| 404/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. 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>Are neither of these two images particularly 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 neither of these two images particularly realistic?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are neither of these two images particularly realistic?\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([-30.5469], 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]: 0.5594,[Response]: B.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 404:  41%|▍| 405/999 
[Running Accuracy]: 0.5605,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 405:  41%|▍| 405/999 [04:55<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>Are neither of these two images particularly 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>Is the sharpness of the first image higher 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 sharpness of the first image higher than 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 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([-31.2188], 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]: 0.5605,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 405:  41%|▍| 406/999 [04:56<06:5
[Running Accuracy]: 0.5591,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 406:  41%|▍| 406/999 [04:56<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 first image higher 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 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([-31.0469], 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([-31.2344], 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]: 0.5591,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 406:  41%|▍| 407/999 [04:56<06:4
[Running Accuracy]: 0.5577,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 407:  41%|▍| 407/999 [04:57<06:44
 {'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. 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 the snowflake-like distortion?
A. The ground in the second image
B. The person in the first 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 below is most severely affected by the snowflake-like distortion?
A. The ground in the second image
B. The person in the first image
C. The background in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by the snowflake-like distortion?\nA. The ground in the second image\nB. The person in the first image\nC. The background 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([-31.4844], 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]: 0.5577,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 407:  41%|▍| 408/999 [04:57<06:42
[Running Accuracy]: 0.5564,[Response]: B.<|endoftext|>, [Correct Ans]: The ground in the second image, , [Prog]: 408: 
 {'prompt': "A chat between a curious user and an artificial 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 the snowflake-like distortion?\nA. The ground in the second image\nB. The person in the first image\nC. The background 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. 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([-31.1406], 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]: 0.5564,[Response]: B.<|endoftext|>, [Correct Ans]: The ground in the second image, , [Prog]: 408: 
[Running Accuracy]: 0.5550,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 409:  41%|▍| 409/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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. overexposure
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. overexposure
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. overexposure\nC. noise\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([-30.3281], 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]: 0.5550,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 409:  41%|▍| 410/999 
[Running Accuracy]: 0.5537,[Response]: B.<|endoftext|>, [Correct Ans]: noise, , [Prog]: 410:  41%|▍| 410/999 [04:58<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 there any distortion issues in these two images?\nA. motion blur\nB. overexposure\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>Which part below is not affected by motion blur?
A. The background pedestrian in the first image
B. The stone in the second image
C. The left-side trees 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 motion blur?
A. The background pedestrian in the first image
B. The stone in the second image
C. The left-side trees in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is not affected by motion blur?\nA. The background pedestrian in the first image\nB. The stone in the second image\nC. The left-side trees in the first 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([-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]: 0.5537,[Response]: B.<|endoftext|>, [Correct Ans]: noise, , [Prog]: 410:  41%|▍| 411/999 [04:59<06
[Running Accuracy]: 0.5547,[Response]: B.<|endoftext|>, [Correct Ans]: The stone in the second image, , [Prog]: 411:  
 {'prompt': "A chat between a curious user and an artificial 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 motion blur?\nA. The background pedestrian in the first image\nB. The stone in the second image\nC. The left-side trees 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 motion blur?
A. Ground in the second image
B. Train in the first image
C. Ground in the first image
D. Shoes 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. Ground in the second image
B. Train in the first image
C. Ground in the first image
D. Shoes 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. Ground in the second image\nB. Train in the first image\nC. Ground in the first image\nD. Shoes 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([-30.2031], 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]: 0.5547,[Response]: B.<|endoftext|>, [Correct Ans]: The stone in the second image, , [Prog]: 411:  
[Running Accuracy]: 0.5558,[Response]: B.<|endoftext|>, [Correct Ans]: Train in the first image, , [Prog]: 412:  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 motion blur?\nA. Ground in the second image\nB. Train in the first image\nC. Ground in the first image\nD. Shoes 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 motion blur?
A. Train in the first image
B. Ground in the first image
C. 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 below is most severely affected by motion blur?
A. Train in the first image
B. Ground in the first image
C. Cat 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. Train in the first image\nB. Ground in the first image\nC. Cat 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([-29.9062], 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]: 0.5558,[Response]: B.<|endoftext|>, [Correct Ans]: Train in the first image, , [Prog]: 412:  41%|▍
[Running Accuracy]: 0.5569,[Response]: A.<|endoftext|>, [Correct Ans]: Train in the first image, , [Prog]: 413:  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 motion blur?\nA. Train in the first image\nB. Ground in the first image\nC. 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>Compared to the second image, how true is 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 second image, how true is 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 second image, how true is 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([-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([-31.0312], 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]: 0.5569,[Response]: A.<|endoftext|>, [Correct Ans]: Train in the first image, , [Prog]: 413:  41%|▍
[Running Accuracy]: 0.5556,[Response]: B.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 414:  41%|▍| 414/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 true is 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>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([-31.2969], 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([-31.3438], 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]: 0.5556,[Response]: B.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 414:  42%|▍| 415/999 
[Running Accuracy]: 0.5542,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 415:  42%|▍| 415/999 [05:02<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 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 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([-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([-30.5469], 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]: 0.5542,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 415:  42%|▍| 416/999 [05:03<06:4
[Running Accuracy]: 0.5553,[Response]: A.<|endoftext|>, [Correct Ans]: More vivid, , [Prog]: 416:  42%|▍| 416/999 [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>Are both of these images not quite 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 not quite realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images not quite realistic?\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([-31.0156], 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]: 0.5553,[Response]: A.<|endoftext|>, [Correct Ans]: More vivid, , [Prog]: 416:  42%|▍| 417/999 [05:
[Running Accuracy]: 0.5564,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 417:  42%|▍| 417/999 [05:03<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 both of these images not quite 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>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([-31.1562], 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])
 B.

[Running Accuracy]: 0.5564,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 417:  42%|▍| 418/999 [05:04<06:3
[Running Accuracy]: 0.5574,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 418:  42%|▍| 418/999 [05:04<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 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>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([-30.9062], 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]: 0.5574,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 418:  42%|▍| 419/999 [05:05<06:2
[Running Accuracy]: 0.5561,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 419:  42%|▍| 419/999 [05:05<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>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 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([-30.9688], 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([-31.0938], 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]: 0.5561,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 419:  42%|▍| 420/999 [05:05<07:12
[Running Accuracy]: 0.5571,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 420:  42%|▍| 420/999 [05:05<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 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 texture detail 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 texture detail 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 texture detail 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([-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([-30.5156], 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]: 0.5571,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 420:  42%|▍| 421/999 [05:06<06:5
[Running Accuracy]: 0.5582,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 421:  42%|▍| 421/999 [05:06<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 texture detail 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>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([-31.2188], 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([-31.2344], 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]: 0.5582,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 421:  42%|▍| 422/999 [05:07<06:5
[Running Accuracy]: 0.5569,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 422:  42%|▍| 422/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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>Have both of these images encountered the issue of out-of-focus?
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 encountered the issue of out-of-focus?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Have both of these images encountered the issue of out-of-focus?\nA. Yes\nB. No\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([-31.5469], 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]: 0.5569,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 422:  42%|▍| 423/999 
[Running Accuracy]: 0.5579,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 423:  42%|▍| 423/999 [05:08<06:49
 {'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>Have both of these images encountered the issue of out-of-focus?\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. 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. Sharper
B. Blurrier
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. Sharper\nB. Blurrier\nC. Similar\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([-31.3594], 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]: 0.5579,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 423:  42%|▍| 424/999 [05:08<06:44
[Running Accuracy]: 0.5590,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 424:  42%|▍| 424/999 [05:08
 {'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. Sharper\nB. Blurrier\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>Which part is most affected by motion blur?
A. The right side bicycle in the second image
B. The person 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 is most affected by motion blur?
A. The right side bicycle in the second image
B. The person 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 is most affected by motion blur?\nA. The right side bicycle in the second image\nB. The person 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([-31.5625], 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([-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]: 0.5590,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 424:  43%|▍| 425/999 [05:09
[Running Accuracy]: 0.5600,[Response]: A.<|endoftext|>, [Correct Ans]: The right side bicycle 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 affected by motion blur?\nA. The right side bicycle in the second image\nB. The person 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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. Background in the second image
C. Cat 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. Ground in the first image
B. Background in the second image
C. Cat in the first 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. Background in the second image\nC. Cat 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([-31.1562], 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]: 0.5600,[Response]: A.<|endoftext|>, [Correct Ans]: The right side bicycle in the second image, , [
[Running Accuracy]: 0.5610,[Response]: C.<|endoftext|>, [Correct Ans]: Cat in the first image, , [Prog]: 426:  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 has the richest texture details?\nA. Ground in the first image\nB. Background in the second image\nC. Cat 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 the texture details of the second image richer than those 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:Are the texture details of the second image richer than those of the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the texture details of the second image richer than those of the first 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([-30.3281], 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]: 0.5610,[Response]: C.<|endoftext|>, [Correct Ans]: Cat in the first image, , [Prog]: 426:  43%|▍| 
[Running Accuracy]: 0.5621,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 427:  43%|▍| 427/999 [05:10<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 the second image richer than those 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 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([-30.8438], 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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.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]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 427:  43%|▍| 428/999 [05:11<06:1
[Running Accuracy]: 0.5631,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 428:  43%|▍| 428/999 [05:11<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 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 is most severely affected by noise?
A. The pig's head in the first image
B. The background 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 severely affected by noise?
A. The pig's head in the first image
B. The background 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 severely affected by noise?\nA. The pig's head in the first image\nB. The background 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([-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([-30.5625], 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]: 0.5631,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 428:  43%|▍| 429/999 [05:11<06:1
[Running Accuracy]: 0.5618,[Response]: A.<|endoftext|>, [Correct Ans]: The cat in the second image, , [Prog]: 429:  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 is most severely affected by noise?\nA. The pig's head in the first image\nB. The background 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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([-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]: 0.5618,[Response]: A.<|endoftext|>, [Correct Ans]: The cat in the second image, , [Prog]: 429:  43
[Running Accuracy]: 0.5628,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 430:  43%|▍| 430/999 [05:12<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. 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 clearer?
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 is clearer?
A. First image
B. Second image
Answer with the option's letter from the given choices directly.

prompts: [["Which image below is clearer?\nA. First image\nB. 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([-31.3281], 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]: 0.5628,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 430:  43%|▍| 431/999 [05:13<06:5
[Running Accuracy]: 0.5615,[Response]: B.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 431:  43%|▍| 431/999 [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 is clearer?\nA. First image\nB. 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 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([-31.2812], 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])
 B.

[Running Accuracy]: 0.5615,[Response]: B.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 431:  43%|▍| 432/999 [05
[Running Accuracy]: 0.5625,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 432:  43%|▍| 432/999 [05:14<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 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 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([-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])
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]: 0.5625,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 432:  43%|▍| 433/999 [05:14<06:2
[Running Accuracy]: 0.5612,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 433:  43%|▍| 433/999 [05:14<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 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 is most affected by motion blur?
A. The person riding an electric bike in the second image
B. The background of 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 is most affected by motion blur?
A. The person riding an electric bike in the second image
B. The background of the second image
C. The characters 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 person riding an electric bike in the second image\nB. The background of the second image\nC. The characters in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.2500], 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])
 A.

[Running Accuracy]: 0.5612,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 433:  43%|▍| 434/999 [05:15<06:2
[Running Accuracy]: 0.5599,[Response]: A.<|endoftext|>, [Correct Ans]: The background of the second image, , [Prog]: 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 riding an electric bike in the second image\nB. The background of the second image\nC. The characters 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([-30.7969], 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([-30.4219], 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]: 0.5599,[Response]: A.<|endoftext|>, [Correct Ans]: The background of the second image, , [Prog]: 4
[Running Accuracy]: 0.5609,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 435:  44%|▍| 435/999 [05:16<06:15
 {'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>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 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([-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([-30.8281], 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]: 0.5609,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 435:  44%|▍| 436/999 [05:16<06:09
[Running Accuracy]: 0.5619,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 436:  44%|▍| 436/999 [05:16<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>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 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([-31.3594], 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([-31.1875], 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]: 0.5619,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 436:  44%|▍| 437/999 [05:17<06:05
[Running Accuracy]: 0.5629,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 437:  44%|▍| 437/999 [05:17<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 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 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([-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])
 A.

[Running Accuracy]: 0.5629,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 437:  44%|▍| 438/999 [05:18<06:1
[Running Accuracy]: 0.5639,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 438:  44%|▍| 438/999 [05:18<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 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>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([-30.8125], 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([-31.1406], 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]: 0.5639,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 438:  44%|▍| 439/999 [05:18<06:0
[Running Accuracy]: 0.5649,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 439:  44%|▍| 439/999 [05:18<06:03
 {'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 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 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([-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]: 0.5649,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 439:  44%|▍| 440/999 [05:19<05:59
[Running Accuracy]: 0.5636,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 440:  44%|▍| 440/999 [05:19<05:59
 {'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 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>Is the second image more lifelike 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 lifelike 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 lifelike than the first image?\nA. No\nB. Yes\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([-31.4219], 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]: 0.5636,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 440:  44%|▍| 441/999 [05:20<06:05
[Running Accuracy]: 0.5646,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 441:  44%|▍| 441/999 [05:20<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 more lifelike 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>Which part below is most severely affected by overexposure?
A. Background trees in the second image
B. Leaves 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 most severely affected by overexposure?
A. Background trees in the second image
B. Leaves 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 most severely affected by overexposure?\nA. Background trees in the second image\nB. Leaves in the first image\nC. Ground 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([-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]: 0.5646,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 441:  44%|▍| 442/999 [05:21<07:1
[Running Accuracy]: 0.5656,[Response]: B.<|endoftext|>, [Correct Ans]: Leaves in the first image, , [Prog]: 442:  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>Which part below is most severely affected by overexposure?\nA. Background trees in the second image\nB. Leaves in the first 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>Compared to the first image, how is the sharpness 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 sharpness 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 sharpness 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([-31.0312], 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])
 C.

[Running Accuracy]: 0.5656,[Response]: B.<|endoftext|>, [Correct Ans]: Leaves in the first image, , [Prog]: 442:  44%|
[Running Accuracy]: 0.5666,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 443:  44%|▍| 443/999 [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>Compared to the first image, how is the sharpness 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>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([-31.2031], 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]: 0.5666,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 443:  44%|▍| 444/999 [05:23<
[Running Accuracy]: 0.5676,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 444:  44%|▍| 444/999 [05:23<08:06
 {'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 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>Which part below is most severely affected by overexposure?
A. The sky in the first image
B. The left side of the sky in the second image
C. The trees 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 first image
B. The left side of the sky in the second image
C. The trees 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 first image\nB. The left side of the sky in the second image\nC. The trees 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([-30.1250], 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]: 0.5676,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 444:  45%|▍| 445/999 [05:23<07:58
[Running Accuracy]: 0.5663,[Response]: A.<|endoftext|>, [Correct Ans]: The left side 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 is most severely affected by overexposure?\nA. The sky in the first image\nB. The left side of the sky in the second image\nC. The trees 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 image is most 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 most 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 most 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([-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([-30.5312], 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]: 0.5663,[Response]: A.<|endoftext|>, [Correct Ans]: The left side of the sky in the second image, ,
[Running Accuracy]: 0.5650,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 446:  45%|▍| 446/999 [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 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>Which part below is most severely affected by overexposure?
A. Figures in the first image
B. Figures in the second image
C. Upper wall in the first 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 part below is most severely affected by overexposure?
A. Figures in the first image
B. Figures in the second image
C. Upper wall in the first image
D. 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 overexposure?\nA. Figures in the first image\nB. Figures in the second image\nC. Upper wall in the first image\nD. Background in the second 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([-31.1250], 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]: 0.5650,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 446:  45%|▍| 447/999 [0
[Running Accuracy]: 0.5660,[Response]: C.<|endoftext|>, [Correct Ans]: Upper wall in the first image, , [Prog]: 447:  
 {'prompt': "A chat between a curious user and an artificial 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. Figures in the first image\nB. Figures in the second image\nC. Upper wall in the first image\nD. Background 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 does the authenticity of the second image differ?
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 differ?
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 differ?\nA. Similar\nB. More authentic\nC. Less authentic\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([-31.3750], 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]: 0.5660,[Response]: C.<|endoftext|>, [Correct Ans]: Upper wall in the first image, , [Prog]: 447:  
[Running Accuracy]: 0.5670,[Response]: C.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 448:  45%|▍| 448/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 differ?\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>Are there any distortions 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:Are there any distortions in these two images?
A. Noise
B. Motion blur
C. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["Are there any distortions 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([-30.6094], 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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 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]: C.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 448:  45%|▍| 449/999 
[Running Accuracy]: 0.5657,[Response]: C.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 449:  45%|▍| 449/999 [05:27<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 distortions 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>Compared to the first image, how is the color richness of the second image?
A. Richer
B. Similar
C. Simpler
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. Simpler
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. Simpler\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([-30.0781], 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]: 0.5657,[Response]: C.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 449:  45%|▍| 450/999 [05:28<07
[Running Accuracy]: 0.5644,[Response]: A.<|endoftext|>, [Correct Ans]: Simpler, , [Prog]: 450:  45%|▍| 450/999 [05:28<
 {'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 color richness of the second image?\nA. Richer\nB. Similar\nC. Simpler\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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. Pedestrian in the first image
B. Ground in the second image
C. Person in the second image
D. Logo in the bottom left corner 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. Pedestrian in the first image
B. Ground in the second image
C. Person in the second image
D. Logo in the bottom left corner 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. Pedestrian in the first image\nB. Ground in the second image\nC. Person in the second image\nD. Logo in the bottom left corner of the first image\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([-31.1875], 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]: 0.5644,[Response]: A.<|endoftext|>, [Correct Ans]: Simpler, , [Prog]: 450:  45%|▍| 451/999 [05:28<
[Running Accuracy]: 0.5632,[Response]: A.<|endoftext|>, [Correct Ans]: Logo in the bottom left corner of the first ima
 {'prompt': "A chat between a curious user and an artificial 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. Pedestrian in the first image\nB. Ground in the second image\nC. Person in the second image\nD. Logo in the bottom left corner 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 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([-31.0312], 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]: 0.5632,[Response]: A.<|endoftext|>, [Correct Ans]: Logo in the bottom left corner of the first ima
[Running Accuracy]: 0.5619,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 452:  45%|▍| 452/999 [05:29<
 {'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>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>Compared to the first image, how is the lighting 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 lighting 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 lighting 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([-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([-31.1250], 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]: 0.5619,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 452:  45%|▍| 453/999 [05:30<
[Running Accuracy]: 0.5607,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 453:  45%|▍| 453/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. 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>Which part below is most severely affected by overexposure?
A. Ground in the first image
B. Leaves in the first image
C. Lamp in the second image
D. 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. Ground in the first image
B. Leaves in the first image
C. Lamp in the second image
D. 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. Ground in the first image\nB. Leaves in the first image\nC. Lamp in the second image\nD. Ground 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([-30.7656], 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]: 0.5607,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 453:  45%|▍| 454/999 
[Running Accuracy]: 0.5617,[Response]: C.<|endoftext|>, [Correct Ans]: Lamp in the second image, , [Prog]: 454:  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>Which part below is most severely affected by overexposure?\nA. Ground in the first image\nB. Leaves in the first image\nC. Lamp in the second image\nD. 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>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([-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([-31.2969], 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]: 0.5617,[Response]: C.<|endoftext|>, [Correct Ans]: Lamp in the second image, , [Prog]: 454:  46%|▍
[Running Accuracy]: 0.5626,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 455:  46%|▍| 455/999 [05:31<06:55
 {'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>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 issues do not exist in these two images?
A. motion blur
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 issues do not exist in these two images?
A. motion blur
B. noise
C. overexposure
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion issues do not exist in these two images?\nA. motion blur\nB. noise\nC. overexposure\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]: 0.5626,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 455:  46%|▍| 456/999 [05:32<06:32
[Running Accuracy]: 0.5614,[Response]: B.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 456:  46%|▍| 456/999 [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 issues do not exist in these two images?\nA. motion blur\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>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([-30.8438], 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]: 0.5614,[Response]: B.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 456:  46%|▍| 457/999 [0
[Running Accuracy]: 0.5602,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 457:  46%|▍| 457/999 [05:33<06:14
 {'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 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 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([-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([-31.0469], 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]: No, , [Prog]: 457:  46%|▍| 458/999 [05:33<06:06
[Running Accuracy]: 0.5611,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 458:  46%|▍| 458/999 [05:33<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 both of these images relatively 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>Which image has been affected by snowflake-like distortion?
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 been affected by snowflake-like distortion?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.

prompts: [["Which image has been affected by snowflake-like distortion?\nA. Second image\nB. First image\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([-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]: 0.5611,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 458:  46%|▍| 459/999 [05:34<06:4
[Running Accuracy]: 0.5599,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 459:  46%|▍| 459/999 [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 has been affected by snowflake-like distortion?\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 illumination of 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 illumination of 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 illumination of 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([-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([-30.2812], 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]: 0.5599,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 459:  46%|▍| 460/999 [05
[Running Accuracy]: 0.5587,[Response]: B.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 460:  46%|▍| 460/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 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>Which part below is most affected by noise?
A. The ground in the second image
B. The character in the first image
C. The doll 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 ground in the second image
B. The character in the first image
C. The doll 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 ground in the second image\nB. The character in the first image\nC. The doll 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([-31.4219], 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]: 0.5587,[Response]: B.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 460:  46%|▍| 461/999
[Running Accuracy]: 0.5575,[Response]: A.<|endoftext|>, [Correct Ans]: The character in the first image, , [Prog]: 461
 {'prompt': "A chat between a curious user and an artificial 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 second image\nB. The character in the first image\nC. The doll 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. 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([-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])
 C.

[Running Accuracy]: 0.5575,[Response]: A.<|endoftext|>, [Correct Ans]: The character in the first image, , [Prog]: 461
[Running Accuracy]: 0.5584,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 462:  46%|▍| 462/999 [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>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 would you rate the realism of 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 would you rate the realism of 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 would you rate the realism of 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([-30.8906], 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([-30.8281], 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]: 0.5584,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 462:  46%|▍| 463/999 [05:37<
[Running Accuracy]: 0.5594,[Response]: C.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 463:  46%|▍| 463/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. 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. Noise
B. Out of focus
C. Overexposure
D. 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 distortion issues in these two images?
A. Noise
B. Out of focus
C. Overexposure
D. Motion blur
Answer with the option's letter from the given choices directly.

prompts: [["Are there any distortion issues in these two images?\nA. Noise\nB. Out of focus\nC. Overexposure\nD. Motion blur\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([-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]: 0.5594,[Response]: C.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 463:  46%|▍| 464/999 
[Running Accuracy]: 0.5582,[Response]: B.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 464:  46%|▍| 464/999 [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 there any distortion issues in these two images?\nA. Noise\nB. Out of focus\nC. Overexposure\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>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([-30.7031], 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([-31.3438], 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]: 0.5582,[Response]: B.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 464:  47%|▍| 465/999 [05
[Running Accuracy]: 0.5591,[Response]: A.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 465:  47%|▍| 465/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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>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([-31.2031], 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])
 B.

[Running Accuracy]: 0.5591,[Response]: A.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 465:  47%|▍| 466/999
[Running Accuracy]: 0.5579,[Response]: B.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 466:  47%|▍| 466/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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>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([-31.2344], 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]: 0.5579,[Response]: B.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 466:  47%|▍| 467/999
[Running Accuracy]: 0.5589,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 467:  47%|▍| 467/999 [05:40<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 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>Compared to the first image, how is the realism of 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 is the realism of 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 is the realism of 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([-30.0312], 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])
 B.

[Running Accuracy]: 0.5589,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 467:  47%|▍| 468/999 [05:40<05:4
[Running Accuracy]: 0.5598,[Response]: B.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 468:  47%|▍| 468/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. 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 would you rate the authenticity of the second image?
A. Almost the same
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 would you rate the authenticity of the second image?
A. Almost the same
B. Less authentic
C. More 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. Almost the same\nB. Less authentic\nC. More authentic\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([-30.8906], 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]: 0.5598,[Response]: B.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 468:  47%|▍| 469/999 
[Running Accuracy]: 0.5586,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 469:  47%|▍| 469/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. Almost the same\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>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([-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([-31.1719], 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]: 0.5586,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 469:  47%|▍| 470/999 
[Running Accuracy]: 0.5596,[Response]: B.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 470:  47%|▍| 470/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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>Which part is most severely affected by snowflake noise?
A. Ground of the first image
B. Sky of the first image
C. Tabletop of the second image
D. Figure 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 severely affected by snowflake noise?
A. Ground of the first image
B. Sky of the first image
C. Tabletop of the second image
D. Figure of the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most severely affected by snowflake noise?\nA. Ground of the first image\nB. Sky of the first image\nC. Tabletop of the second image\nD. Figure of 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([-30.4531], 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]: 0.5596,[Response]: B.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 470:  47%|▍| 471/999 
[Running Accuracy]: 0.5605,[Response]: A.<|endoftext|>, [Correct Ans]: Ground of the first image, , [Prog]: 471:  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>Which part is most severely affected by snowflake noise?\nA. Ground of the first image\nB. Sky of the first image\nC. Tabletop of the second image\nD. Figure 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>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([-31.3281], 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([-30.3594], 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]: 0.5605,[Response]: A.<|endoftext|>, [Correct Ans]: Ground of the first image, , [Prog]: 471:  47%|
[Running Accuracy]: 0.5614,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 472:  47%|▍| 472/999 [05:43
 {'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>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 realism 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 realism 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 realism 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([-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([-31.2500], 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]: 0.5614,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 472:  47%|▍| 473/999 [05:44
[Running Accuracy]: 0.5624,[Response]: B.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 473:  47%|▍| 473/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. 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 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([-31.2500], 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([-31.5000], 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]: 0.5624,[Response]: B.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 473:  47%|▍| 474/999 
[Running Accuracy]: 0.5633,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 474:  47%|▍| 474/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 pool in the first image
B. The train 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 severely affected by motion blur?
A. The pool in the first image
B. The train 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 severely affected by motion blur?\nA. The pool in the first image\nB. The train 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([-30.8750], 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([-31.0938], 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]: 0.5633,[Response]: B.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 474:  48%|▍| 475/999 
[Running Accuracy]: 0.5642,[Response]: B.<|endoftext|>, [Correct Ans]: The train in the second image, , [Prog]: 475:  
 {'prompt': "A chat between a curious user and an artificial 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 pool in the first image\nB. The train 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 type of distortion did these two images not suffer from?
A. Out-of-focus
B. Noise
C. Overexposure
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 did these two images not suffer from?
A. Out-of-focus
B. Noise
C. Overexposure
D. Motion blur
Answer with the option's letter from the given choices directly.

prompts: [["Which type of distortion did these two images not suffer from?\nA. Out-of-focus\nB. Noise\nC. Overexposure\nD. Motion 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([-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])
 C.

[Running Accuracy]: 0.5642,[Response]: B.<|endoftext|>, [Correct Ans]: The train in the second image, , [Prog]: 475:  
[Running Accuracy]: 0.5630,[Response]: C.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 476:  48%|▍| 476/999 [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 type of distortion did these two images not suffer from?\nA. Out-of-focus\nB. Noise\nC. Overexposure\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>Which image has better 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 better lighting?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.

prompts: [["Which image has better lighting?\nA. Second image\nB. First image\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([-30.2344], 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]: 0.5630,[Response]: C.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 476:  48%|▍| 477/999 [05
[Running Accuracy]: 0.5639,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 477:  48%|▍| 477/999 [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 better 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>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([-31.2188], 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([-31.1094], 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]: 0.5639,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 477:  48%|▍| 478/999 [0
[Running Accuracy]: 0.5628,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 478:  48%|▍| 478/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 of the following images has a certain overexposure problem?
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 certain overexposure problem?
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 certain overexposure problem?\nA. Second image\nB. 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([-31.3125], 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]: 0.5628,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 478:  48%|▍| 479/999 
[Running Accuracy]: 0.5637,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 479:  48%|▍| 479/999 [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 certain overexposure problem?\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 both of these images a bit 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 a bit blurry?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images a bit 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([-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]: 0.5637,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 479:  48%|▍| 480/999 [0
[Running Accuracy]: 0.5646,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 480:  48%|▍| 480/999 [05:49<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>Are both of these images a bit 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. 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([-30.7812], 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([-31.1406], 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]: 0.5646,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 480:  48%|▍| 481/999 [05:49<06:1
[Running Accuracy]: 0.5655,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 481:  48%|▍| 481/999 [05:49<
 {'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 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 lighting 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 lighting 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 lighting 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([-31.1094], 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([-30.6875], 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]: 0.5655,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 481:  48%|▍| 482/999 [05:50<
[Running Accuracy]: 0.5664,[Response]: A.<|endoftext|>, [Correct Ans]: Less adequate, , [Prog]: 482:  48%|▍| 482/999 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. About the same\nC. More adequate\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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([-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])
 B.

[Running Accuracy]: 0.5664,[Response]: A.<|endoftext|>, [Correct Ans]: Less adequate, , [Prog]: 482:  48%|▍| 483/999 [
[Running Accuracy]: 0.5673,[Response]: B.<|endoftext|>, [Correct Ans]: Less vivid, , [Prog]: 483:  48%|▍| 483/999 [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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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([-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([-31.2344], 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]: 0.5673,[Response]: B.<|endoftext|>, [Correct Ans]: Less vivid, , [Prog]: 483:  48%|▍| 484/999 [05:
[Running Accuracy]: 0.5682,[Response]: C.<|endoftext|>, [Correct Ans]: More Rich, , [Prog]: 484:  48%|▍| 484/999 [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>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>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([-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([-31.1094], 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]: 0.5682,[Response]: C.<|endoftext|>, [Correct Ans]: More Rich, , [Prog]: 484:  49%|▍| 485/999 [05:5
[Running Accuracy]: 0.5691,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 485:  49%|▍| 485/999 [05:52<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 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>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([-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])
 B.

[Running Accuracy]: 0.5691,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 485:  49%|▍| 486/999 [05:53<06:29
[Running Accuracy]: 0.5700,[Response]: B.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 486:  49%|▍| 486/999 [05:53<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>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 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([-30.7188], 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([-31.2969], 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]: 0.5700,[Response]: B.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 486:  49%|▍| 487/999 [05:54<0
[Running Accuracy]: 0.5688,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 487:  49%|▍| 487/999 [05:54<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 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>How would you rate the sharpness of the second image compared to the first 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:How would you rate the sharpness of the second image compared to the first image?
A. Sharper
B. Blurrier
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["How would you rate the sharpness of the second image compared to the first image?\nA. Sharper\nB. Blurrier\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([-30.9531], 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]: 0.5688,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 487:  49%|▍| 488/999 [05:54<06:0
[Running Accuracy]: 0.5697,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 488:  49%|▍| 488/999 [05:54
 {'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 would you rate the sharpness of the second image compared to the first 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 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([-31.2656], 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([-31.1406], 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]: 0.5697,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 488:  49%|▍| 489/999 [05:55
[Running Accuracy]: 0.5706,[Response]: B.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 489:  49%|▍| 489/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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>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([-30.8125], 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]: 0.5706,[Response]: B.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 489:  49%|▍| 490/999 
[Running Accuracy]: 0.5694,[Response]: B.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 490:  49%|▍| 490/999 [05:56<
 {'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>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 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:Compared 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: [["Compared 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([-31.1875], 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([-30.9219], 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]: 0.5694,[Response]: B.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 490:  49%|▍| 491/999 [05:57<
[Running Accuracy]: 0.5703,[Response]: C.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 491:  49%|▍| 491/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. 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 sky in the second image
B. The person in the first image
C. The sky in the first 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 motion blur?
A. The sky in the second image
B. The person in the first image
C. The sky in the first 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 motion blur?\nA. The sky in the second image\nB. The person in the first image\nC. The sky in the first image\nD. The ground 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([-31.2812], 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]: 0.5703,[Response]: C.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 491:  49%|▍| 492/999 
[Running Accuracy]: 0.5711,[Response]: B.<|endoftext|>, [Correct Ans]: The person in the first image, , [Prog]: 492:  
 {'prompt': "A chat between a curious user and an artificial 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 sky in the second image\nB. The person in the first image\nC. The sky in the first image\nD. The 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>Are the colors of these two images both relatively 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 relatively 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 relatively vivid?\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([-31.2969], 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]: 0.5711,[Response]: B.<|endoftext|>, [Correct Ans]: The person in the first image, , [Prog]: 492:  
[Running Accuracy]: 0.5720,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 493:  49%|▍| 493/999 [05:58<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 colors of these two images both relatively vivid?\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([-31.2500], 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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5720,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 493:  49%|▍| 494/999 [05:59<05:5
[Running Accuracy]: 0.5729,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 494:  49%|▍| 494/999 [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. 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>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([-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([-30.6094], 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]: 0.5729,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 494:  50%|▍| 495/999 [05
[Running Accuracy]: 0.5737,[Response]: C.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 495:  50%|▍| 495/999 [05:59<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. 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 part is more severely affected by motion blur?
A. Background of the first image
B. Motorcycle in the second image
C. Characters in the first image
D. 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 is more severely affected by motion blur?
A. Background of the first image
B. Motorcycle in the second image
C. Characters in the first image
D. Background of the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is more severely affected by motion blur?\nA. Background of the first image\nB. Motorcycle in the second image\nC. Characters in the first image\nD. 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([-31.0938], 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]: 0.5737,[Response]: C.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 495:  50%|▍| 496/999 [06:00<0
[Running Accuracy]: 0.5746,[Response]: B.<|endoftext|>, [Correct Ans]: Motorcycle in the second image, , [Prog]: 496: 
 {'prompt': "A chat between a curious user and an artificial 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 more severely affected by motion blur?\nA. Background of the first image\nB. Motorcycle in the second image\nC. Characters in the first image\nD. 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>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([-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])
 B.

[Running Accuracy]: 0.5746,[Response]: B.<|endoftext|>, [Correct Ans]: Motorcycle in the second image, , [Prog]: 496: 
[Running Accuracy]: 0.5734,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 497:  50%|▍| 497/999 [06:01<05:49
 {'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>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>Are the colors of these two images both monotonous?
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 monotonous?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the colors of these two images both monotonous?\nA. Yes\nB. No\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([-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]: 0.5734,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 497:  50%|▍| 498/999 [06:01<05:45
[Running Accuracy]: 0.5723,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 498:  50%|▍| 498/999 [06:01<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>Are the colors of these two images both monotonous?\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([-31.4219], 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([-31.3125], 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]: 0.5723,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 498:  50%|▍| 499/999 [06:02<05:36
[Running Accuracy]: 0.5711,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 499:  50%|▍| 499/999 [06:02<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 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 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([-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([-30.7969], 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]: 0.5711,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 499:  50%|▌| 500/999 [06:03<05:3
[Running Accuracy]: 0.5700,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 500:  50%|▌| 500/999 [06:03<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 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>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([-31.1094], 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]: 0.5700,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 500:  50%|▌| 501/999 [06:03<05:2
[Running Accuracy]: 0.5709,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 501:  50%|▌| 501/999 [06:03<05:23
 {'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. 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 affected by motion blur?
A. The hand of the person in the second image
B. The tabletop in the second image
C. The sky in the first image
D. The boat 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 hand of the person in the second image
B. The tabletop in the second image
C. The sky in the first image
D. The boat 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 hand of the person in the second image\nB. The tabletop in the second image\nC. The sky in the first image\nD. The boat 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([-31.1875], 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]: 0.5709,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 501:  50%|▌| 502/999 [06:04<05:30
[Running Accuracy]: 0.5717,[Response]: A.<|endoftext|>, [Correct Ans]: The hand of the person 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 affected by motion blur?\nA. The hand of the person in the second image\nB. The tabletop in the second image\nC. The sky in the first image\nD. The boat 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 illumination of these two images insufficient?
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 these two images insufficient?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the illumination of these two images insufficient?\nA. Yes\nB. No\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([-30.5938], 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]: 0.5717,[Response]: A.<|endoftext|>, [Correct Ans]: The hand of the person in the second image, , [
[Running Accuracy]: 0.5726,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 503:  50%|▌| 503/999 [06:05<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 illumination of these two images insufficient?\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([-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([-30.8281], 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]: 0.5726,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 503:  50%|▌| 504/999 [06:06<06:0
[Running Accuracy]: 0.5714,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 504:  50%|▌| 504/999 [06:06<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 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 most severely affected by motion blur?
A. Background of the first image
B. Plants in the second image
C. Aircraft in the first image
D. 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 motion blur?
A. Background of the first image
B. Plants in the second image
C. Aircraft in the first image
D. 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 motion blur?\nA. Background of the first image\nB. Plants in the second image\nC. Aircraft in the first image\nD. Ground 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([-30.9531], 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]: 0.5714,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 504:  51%|▌| 505/999 [06:06<05:5
[Running Accuracy]: 0.5703,[Response]: B.<|endoftext|>, [Correct Ans]: Background of the first image, , [Prog]: 505:  
 {'prompt': "A chat between a curious user and an artificial 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. Plants in the second image\nC. Aircraft in the first image\nD. 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>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([-31.3125], 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([-31.3438], 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]: 0.5703,[Response]: B.<|endoftext|>, [Correct Ans]: Background of the first image, , [Prog]: 505:  
[Running Accuracy]: 0.5692,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 506:  51%|▌| 506/999 [06:07<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>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>Are there any distortion issues in these two images?
A. Motion blur
B. Out of focus
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. Out of focus
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. Out of focus\nC. Noise\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([-31.1094], 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]: 0.5692,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 506:  51%|▌| 507/999 [06:08<05:33
[Running Accuracy]: 0.5680,[Response]: B.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 507:  51%|▌| 507/999 [06:08<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 there any distortion issues in these two images?\nA. Motion blur\nB. Out of focus\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>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([-31.3594], 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([-30.4688], 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]: 0.5680,[Response]: B.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 507:  51%|▌| 508/999 [06:08<05
[Running Accuracy]: 0.5689,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 508:  51%|▌| 508/999 [06:08<05:25
 {'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 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>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.], device='cuda:0', dtype=torch.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]: 0.5689,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 508:  51%|▌| 509/999 [06:09<05:20
[Running Accuracy]: 0.5697,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 509:  51%|▌| 509/999 [06:09<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>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 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([-31.2500], 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([-30.6875], 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]: 0.5697,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 509:  51%|▌| 510/999 [06:10<05:18
[Running Accuracy]: 0.5706,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 510:  51%|▌| 510/999 [06:10<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>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 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:Are both of these images affected by motion blur?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images affected by motion blur?\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([-31.0312], 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]: 0.5706,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 510:  51%|▌| 511/999 [06:10<05:1
[Running Accuracy]: 0.5714,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 511:  51%|▌| 511/999 [06:10<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>Are both of these images 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>Are both of these images a bit 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 a bit blurry?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images a bit 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([-31.3750], 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]: 0.5714,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 511:  51%|▌| 512/999 [06:11<05:0
[Running Accuracy]: 0.5703,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 512:  51%|▌| 512/999 [06:11<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>Are both of these images a bit 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 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([-31.2500], 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([-31.4375], 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]: 0.5703,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 512:  51%|▌| 513/999 [06:12<05:4
[Running Accuracy]: 0.5712,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 513:  51%|▌| 513/999 [06:12<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 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 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([-30.9062], 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]: 0.5712,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 513:  51%|▌| 514/999 [06:12<05:4
[Running Accuracy]: 0.5700,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 514:  51%|▌| 514/999 [06:12<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 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. 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([-30.2031], 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]: 0.5700,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 514:  52%|▌| 515/999 [06:13<05:3
[Running Accuracy]: 0.5689,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 515:  52%|▌| 515/999 [06:13<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 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>Which image below is more severely 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 below is more severely affected by overexposure?
A. First image
B. Second image
Answer with the option's letter from the given choices directly.

prompts: [["Which image below is more severely affected by overexposure?\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([-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]: 0.5689,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 515:  52%|▌| 516/999 [06:14<05:4
[Running Accuracy]: 0.5678,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 516:  52%|▌| 516/999 [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 below is more severely 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. 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([-31.0469], 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([-31.1719], 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]: 0.5678,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 516:  52%|▌| 517/999 [0
[Running Accuracy]: 0.5667,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 517:  52%|▌| 517/999 [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 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 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([-30.5312], 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([-31.1719], 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]: 0.5667,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 517:  52%|▌| 518/999 [0
[Running Accuracy]: 0.5656,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 518:  52%|▌| 518/999 [06:15<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>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([-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]: 0.5656,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 518:  52%|▌| 519/999 [06:16<05:2
[Running Accuracy]: 0.5645,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 519:  52%|▌| 519/999 [06:16<05:2
 {'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 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>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([-31.1875], 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]: 0.5645,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 519:  52%|▌| 520/999 [06:17<05:2
[Running Accuracy]: 0.5654,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 520:  52%|▌| 520/999 [06:17<05:2
 {'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>Is the illumination 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 illumination 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 illumination 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([-31.2812], 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([-31.2969], 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]: 0.5654,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 520:  52%|▌| 521/999 [06:17<05:1
[Running Accuracy]: 0.5643,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 521:  52%|▌| 521/999 [06:17<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 illumination 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 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([-31.1875], 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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.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]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 521:  52%|▌| 522/999 [06:18<05:0
[Running Accuracy]: 0.5632,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 522:  52%|▌| 522/999 [06:18<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>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 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([-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([-31.4062], 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]: 0.5632,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 522:  52%|▌| 523/999 [06:18<05:1
[Running Accuracy]: 0.5621,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 523:  52%|▌| 523/999 [06:18<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>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>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([-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([-30.6719], 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. Yes

[Running Accuracy]: 0.5621,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 523:  52%|▌| 524/999 [06:19<05:1
[Running Accuracy]: 0.5630,[Response]: A. Yes<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 524:  52%|▌| 524/999 [06:19<
 {'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 blurry?\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>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([-30.8906], 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])
 A.

[Running Accuracy]: 0.5630,[Response]: A. Yes<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 524:  53%|▌| 525/999 [06:20<
[Running Accuracy]: 0.5619,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 525:  53%|▌| 525/999 [06:20<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>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([-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([-30.7969], 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]: 0.5619,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 525:  53%|▌| 526/999 [06:20<05:10
[Running Accuracy]: 0.5608,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 526:  53%|▌| 526/999 [06:20<05:10
 {'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 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>Which image below suffers from 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 below suffers from 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 below suffers from 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([-30.8438], 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([-31.1719], 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]: 0.5608,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 526:  53%|▌| 527/999 [06:21<05:26
[Running Accuracy]: 0.5617,[Response]: B.<|endoftext|>, [Correct Ans]: the first image, , [Prog]: 527:  53%|▌| 527/999
 {'prompt': "A chat between a curious user and an artificial 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 second image\nB. 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 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([-31.1406], 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([-31.2812], 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]: 0.5617,[Response]: B.<|endoftext|>, [Correct Ans]: the first image, , [Prog]: 527:  53%|▌| 528/999
[Running Accuracy]: 0.5625,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 528:  53%|▌| 528/999 [06:22<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 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>Are both of these images 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 blurry?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images blurry?\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([-31.2500], 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]: 0.5625,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 528:  53%|▌| 529/999 [06:23<05:2
[Running Accuracy]: 0.5614,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 529:  53%|▌| 529/999 [06:23<05:2
 {'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 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. 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([-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]: 0.5614,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 529:  53%|▌| 530/999 [06:23<05:2
[Running Accuracy]: 0.5623,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 530:  53%|▌| 530/999 [06:23<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>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 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([-31.0156], 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([-30.6406], 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]: 0.5623,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 530:  53%|▌| 531/999 [06:24<05:16
[Running Accuracy]: 0.5631,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 531:  53%|▌| 531/999 [06:24<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>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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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([-30.9531], 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([-30.9375], 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]: 0.5631,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 531:  53%|▌| 532/999 [06:25<05:0
[Running Accuracy]: 0.5639,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 532:  53%|▌| 532/999 [06:25<05:04
 {'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. 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 distortion issues exist in these two images?
A. Out of focus
B. Overexposure
C. Noise
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What distortion issues exist in these two images?
A. Out of focus
B. Overexposure
C. Noise
Answer with the option's letter from the given choices directly.

prompts: [["What distortion issues exist in these two images?\nA. Out of focus\nB. Overexposure\nC. 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([-31.1562], 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]: 0.5639,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 532:  53%|▌| 533/999 [06:25<05:08
[Running Accuracy]: 0.5647,[Response]: A.<|endoftext|>, [Correct Ans]: Out of focus, , [Prog]: 533:  53%|▌| 533/999 [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 issues exist in these two images?\nA. Out of focus\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 image has more serious focus blur issues?
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 serious focus blur issues?
A. the first image
B. the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which image has more serious focus blur issues?\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([-31.1250], 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]: 0.5647,[Response]: A.<|endoftext|>, [Correct Ans]: Out of focus, , [Prog]: 533:  53%|▌| 534/999 [0
[Running Accuracy]: 0.5655,[Response]: A.<|endoftext|>, [Correct Ans]: the first image, , [Prog]: 534:  53%|▌| 534/999
 {'prompt': "A chat between a curious user and an artificial 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 serious focus blur issues?\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>Which image below is less clear?
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 is less clear?
A. the first image
B. the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which image below is less clear?\nA. the first image\nB. 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([-31.2969], 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]: 0.5655,[Response]: A.<|endoftext|>, [Correct Ans]: the first image, , [Prog]: 534:  54%|▌| 535/999
[Running Accuracy]: 0.5664,[Response]: A.<|endoftext|>, [Correct Ans]: the first image, , [Prog]: 535:  54%|▌| 535/999
 {'prompt': "A chat between a curious user and an artificial 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 less clear?\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>Which image has more severe 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 has more severe motion blur?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.

prompts: [["Which image has more severe motion blur?\nA. Second image\nB. First image\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([-30.9062], 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]: 0.5664,[Response]: A.<|endoftext|>, [Correct Ans]: the first image, , [Prog]: 535:  54%|▌| 536/999
[Running Accuracy]: 0.5653,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 536:  54%|▌| 536/999 [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 image has more severe 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 part has the richest texture details?
A. Castle in the second image
B. Background in the first image
C. Grassland in the second image
D. Wall 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. Castle in the second image
B. Background in the first image
C. Grassland in the second image
D. Wall in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the richest texture details?\nA. Castle in the second image\nB. Background in the first image\nC. Grassland in the second image\nD. Wall 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])
 A.

[Running Accuracy]: 0.5653,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 536:  54%|▌| 537/999 [06
[Running Accuracy]: 0.5642,[Response]: A.<|endoftext|>, [Correct Ans]: Wall in the first image, , [Prog]: 537:  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 has the richest texture details?\nA. Castle in the second image\nB. Background in the first image\nC. Grassland in the second image\nD. Wall 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 kind of distortion does not appear in the following two images?
A. mosaic-like distortion
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:Which kind of distortion does not appear in the following two images?
A. mosaic-like distortion
B. overexposure
C. noise
D. motion blur
Answer with the option's letter from the given choices directly.

prompts: [["Which kind of distortion does not appear in the following two images?\nA. mosaic-like distortion\nB. overexposure\nC. noise\nD. motion blur\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([-31.0469], 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]: 0.5642,[Response]: A.<|endoftext|>, [Correct Ans]: Wall in the first image, , [Prog]: 537:  54%|▌|
[Running Accuracy]: 0.5632,[Response]: B.<|endoftext|>, [Correct Ans]: noise, , [Prog]: 538:  54%|▌| 538/999 [06:28<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 kind of distortion does not appear in the following two images?\nA. mosaic-like distortion\nB. overexposure\nC. noise\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>What kind of distortion issues exist in the following two images?
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:What kind of distortion issues exist in the following two images?
A. Motion blur
B. Overexposure
C. Noise
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion issues exist in the following two images?\nA. Motion blur\nB. Overexposure\nC. Noise\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([-31.2031], 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]: 0.5632,[Response]: B.<|endoftext|>, [Correct Ans]: noise, , [Prog]: 538:  54%|▌| 539/999 [06:29<04
[Running Accuracy]: 0.5640,[Response]: A.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 539:  54%|▌| 539/999 [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 issues exist in the following two images?\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 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([-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([-31.3438], 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]: 0.5640,[Response]: A.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 539:  54%|▌| 540/999 [06
[Running Accuracy]: 0.5648,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 540:  54%|▌| 540/999 [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 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>Which image is more severely affected by motion blur?
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 is more severely affected by motion blur?
A. the second image
B. the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which image is more severely affected by motion blur?\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([-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])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5648,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 540:  54%|▌| 541/999 [06
[Running Accuracy]: 0.5638,[Response]: A.<|endoftext|>, [Correct Ans]: the first image, , [Prog]: 541:  54%|▌| 541/999
 {'prompt': "A chat between a curious user and an artificial 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. 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>What kinds of distortion issues exist in the following 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 kinds of distortion issues exist in the following two images?
A. Noise
B. Motion Blur
C. Underexposure
D. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["What kinds of distortion issues exist in the following 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([-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([-31.0469], 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]: 0.5638,[Response]: A.<|endoftext|>, [Correct Ans]: the first image, , [Prog]: 541:  54%|▌| 542/999
[Running Accuracy]: 0.5646,[Response]: B.<|endoftext|>, [Correct Ans]: Motion Blur, , [Prog]: 542:  54%|▌| 542/999 [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 kinds of distortion issues exist in the following 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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([-30.7812], 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([-31.2969], 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]: 0.5646,[Response]: B.<|endoftext|>, [Correct Ans]: Motion Blur, , [Prog]: 542:  54%|▌| 543/999 [06
[Running Accuracy]: 0.5635,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 543:  54%|▌| 543/999 [06:32<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 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 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([-30.5625], 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([-31.5625], 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]: 0.5635,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 543:  54%|▌| 544/999 [06:33<05:3
[Running Accuracy]: 0.5625,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 544:  54%|▌| 544/999 [06:33<05:34
 {'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 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 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([-31.0938], 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([-31.5000], 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]: 0.5625,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 544:  55%|▌| 545/999 [06:33<05:20
[Running Accuracy]: 0.5633,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 545:  55%|▌| 545/999 [06:33<05:2
 {'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 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([-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]: 0.5633,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 545:  55%|▌| 546/999 [06:34<05:2
[Running Accuracy]: 0.5641,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 546:  55%|▌| 546/999 [06:34<05:2
 {'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. 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([-31.1094], 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]: 0.5641,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 546:  55%|▌| 547/999 [06:35<05:1
[Running Accuracy]: 0.5631,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 547:  55%|▌| 547/999 [06:35<05:18
 {'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. 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 blurred?
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 is more blurred?
A. the first image
B. the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which image is more blurred?\nA. the first image\nB. the second image\nAnswer with the option's letter from the given choices directly.\n"]]
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])
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]: 0.5631,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 547:  55%|▌| 548/999 [06:35<05:04
[Running Accuracy]: 0.5620,[Response]: A.<|endoftext|>, [Correct Ans]: the second image, , [Prog]: 548:  55%|▌| 548/99
 {'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 image is more blurred?\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>Which part below is more severely affected by overexposure?
A. The computer screen in the second image
B. The figure 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 more severely affected by overexposure?
A. The computer screen in the second image
B. The figure 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 more severely affected by overexposure?\nA. The computer screen in the second image\nB. The figure 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([-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])
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]: 0.5620,[Response]: A.<|endoftext|>, [Correct Ans]: the second image, , [Prog]: 548:  55%|▌| 549/99
[Running Accuracy]: 0.5628,[Response]: A.<|endoftext|>, [Correct Ans]: The computer screen 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 more severely affected by overexposure?\nA. The computer screen in the second image\nB. The figure 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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?
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?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the second image clearer than the first?\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([-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]: 0.5628,[Response]: A.<|endoftext|>, [Correct Ans]: The computer screen in the second image, , [Pro
[Running Accuracy]: 0.5618,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 550:  55%|▌| 550/999 [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 second image clearer than the first?\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 distortion problems are present in both images?
A. Noise
B. Overexposure
C. Excessive light
D. Underexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What distortion problems are present in both images?
A. Noise
B. Overexposure
C. Excessive light
D. Underexposure
Answer with the option's letter from the given choices directly.

prompts: [["What distortion problems are present in both images?\nA. Noise\nB. Overexposure\nC. Excessive light\nD. Underexposure\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([-31.1719], 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]: 0.5618,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 550:  55%|▌| 551/999 [06:38<05:5
[Running Accuracy]: 0.5608,[Response]: D.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 551:  55%|▌| 551/999 [06:38<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 distortion problems are present in both images?\nA. Noise\nB. Overexposure\nC. Excessive 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>Compared to the first image, how would you rate the clarity of the second image?
A. Much higher clarity
B. Much lower clarity
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 clarity of the second image?
A. Much higher clarity
B. Much lower clarity
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 clarity of the second image?\nA. Much higher clarity\nB. Much lower clarity\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([-31.0312], 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]: 0.5608,[Response]: D.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 551:  55%|▌| 552/999 [06:39<06
[Running Accuracy]: 0.5598,[Response]: C.<|endoftext|>, [Correct Ans]: Much higher clarity, , [Prog]: 552:  55%|▌| 552
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. Much higher clarity\nB. Much lower clarity\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>Did both of these images have motion blur 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 motion blur issues?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Did both of these images have motion blur issues?\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([-30.7969], 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]: 0.5598,[Response]: C.<|endoftext|>, [Correct Ans]: Much higher clarity, , [Prog]: 552:  55%|▌| 553
[Running Accuracy]: 0.5588,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 553:  55%|▌| 553/999 [06:40<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>Did both of these images have motion blur issues?\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 sharpness of the first image?
A. Similar
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 second image, how is the sharpness of the first image?
A. Similar
B. Much clearer
C. Much 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. Similar\nB. Much clearer\nC. Much blurrier\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([-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])
 C.

[Running Accuracy]: 0.5588,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 553:  55%|▌| 554/999 [06:41<06:56
[Running Accuracy]: 0.5596,[Response]: C.<|endoftext|>, [Correct Ans]: Much blurrier, , [Prog]: 554:  55%|▌| 554/999 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. Similar\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>What kind of distortion did not appear in these two images?
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:What kind of distortion did not appear in these two images?
A. motion blur
B. overexposure
C. noise
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion did not appear in these two images?\nA. motion blur\nB. overexposure\nC. noise\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([-30.9062], 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]: 0.5596,[Response]: C.<|endoftext|>, [Correct Ans]: Much blurrier, , [Prog]: 554:  56%|▌| 555/999 [
[Running Accuracy]: 0.5586,[Response]: B.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 555:  56%|▌| 555/999 [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 did not appear in these two images?\nA. motion blur\nB. overexposure\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>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([-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([-30.9688], 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]: 0.5586,[Response]: B.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 555:  56%|▌| 556/999 [06
[Running Accuracy]: 0.5594,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 556:  56%|▌| 556/999 [06:43<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 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>Is the noise in the first image 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 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 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([-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])
 B.

[Running Accuracy]: 0.5594,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 556:  56%|▌| 557/999 [06:44<06:5
[Running Accuracy]: 0.5583,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 557:  56%|▌| 557/999 [06:44<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>Is the noise in the first image 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>Which part below has not been affected by motion blur?
A. The balloon in the first image
B. The motorcycle in the second image
C. The pedestrians in 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 has not been affected by motion blur?
A. The balloon in the first image
B. The motorcycle in the second image
C. The pedestrians in the background of the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below has not been affected by motion blur?\nA. The balloon in the first image\nB. The motorcycle in the second image\nC. The pedestrians in the background of the first 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([-30.7656], 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]: 0.5583,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 557:  56%|▌| 558/999 [06:45<06:52
[Running Accuracy]: 0.5591,[Response]: B.<|endoftext|>, [Correct Ans]: The motorcycle in the second image, , [Prog]: 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 has not been affected by motion blur?\nA. The balloon in the first image\nB. The motorcycle in the second image\nC. The pedestrians in 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>Is the lighting 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 lighting sufficient in these two images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the lighting sufficient in these two images?\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([-31.5312], 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]: 0.5591,[Response]: B.<|endoftext|>, [Correct Ans]: The motorcycle in the second image, , [Prog]: 5
[Running Accuracy]: 0.5599,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 559:  56%|▌| 559/999 [06:46<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 lighting sufficient in 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>Is the second image blurrier than the first?
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 blurrier than the first?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the second image blurrier than the first?\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([-30.7969], 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]: 0.5599,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 559:  56%|▌| 560/999 [06:47<07:1
[Running Accuracy]: 0.5607,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 560:  56%|▌| 560/999 [06:47<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 blurrier than the first?\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 authentic is the first image?
A. Almost the same
B. Much higher authenticity
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 authentic is the first image?
A. Almost the same
B. Much higher authenticity
C. Much lower authenticity
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, how authentic is the first image?\nA. Almost the same\nB. Much higher authenticity\nC. Much lower authenticity\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([-31.2812], 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]: 0.5607,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 560:  56%|▌| 561/999 [06:48<07:5
[Running Accuracy]: 0.5615,[Response]: A.<|endoftext|>, [Correct Ans]: Almost the same, , [Prog]: 561:  56%|▌| 561/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 authentic is the first image?\nA. Almost the same\nB. Much higher authenticity\nC. Much lower authenticity\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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. Slightly blurrier
C. Slightly 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. Slightly blurrier
C. Slightly 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. Slightly blurrier\nC. Slightly 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([-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]: 0.5615,[Response]: A.<|endoftext|>, [Correct Ans]: Almost the same, , [Prog]: 561:  56%|▌| 562/999
[Running Accuracy]: 0.5605,[Response]: B.<|endoftext|>, [Correct Ans]: Slightly clearer, , [Prog]: 562:  56%|▌| 562/99
 {'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>Compared to the first image, how is the clarity of the second image?\nA. Similar\nB. Slightly blurrier\nC. Slightly 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 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([-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])
 B.

[Running Accuracy]: 0.5605,[Response]: B.<|endoftext|>, [Correct Ans]: Slightly clearer, , [Prog]: 562:  56%|▌| 563/99
[Running Accuracy]: 0.5595,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 563:  56%|▌| 563/999 [06:50<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 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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. Similar
B. Much monotonous
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 is the color of the second image?
A. Similar
B. Much monotonous
C. Much richer
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. Similar\nB. Much monotonous\nC. Much richer\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([-31.2969], 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]: 0.5595,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 563:  56%|▌| 564/999 [06:51<07:3
[Running Accuracy]: 0.5585,[Response]: C.<|endoftext|>, [Correct Ans]: Similar, , [Prog]: 564:  56%|▌| 564/999 [06:51<
 {'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>Compared to the first image, how rich is the color of the second image?\nA. Similar\nB. Much monotonous\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>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([-31.1406], 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]: 0.5585,[Response]: C.<|endoftext|>, [Correct Ans]: Similar, , [Prog]: 564:  57%|▌| 565/999 [06:52<
[Running Accuracy]: 0.5575,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 565:  57%|▌| 565/999 [06:52<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 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 not been affected by noise?
A. The grassland in the first image
B. The sofa in the second image
C. The wooden 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 has not been affected by noise?
A. The grassland in the first image
B. The sofa in the second image
C. The wooden wall in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below has not been affected by noise?\nA. The grassland in the first image\nB. The sofa in the second image\nC. The wooden wall 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([-31.0781], 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]: 0.5575,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 565:  57%|▌| 566/999 [06:53<07:2
[Running Accuracy]: 0.5565,[Response]: C.<|endoftext|>, [Correct Ans]: The grassland in the first image, , [Prog]: 566
 {'prompt': "A chat between a curious user and an artificial 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 grassland in the first image\nB. The sofa in the second image\nC. The wooden wall 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 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([-31.4844], 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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.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]: C.<|endoftext|>, [Correct Ans]: The grassland in the first image, , [Prog]: 566
[Running Accuracy]: 0.5573,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 567:  57%|▌| 567/999 [06:54<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 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>Compared to the first image, how is the clarity of the second image?
A. Similar
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. Similar
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. Similar\nB. Much blurrier\nC. Much clearer\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([-30.9531], 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]: 0.5573,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 567:  57%|▌| 568/999 [06:55<07:1
[Running Accuracy]: 0.5581,[Response]: B.<|endoftext|>, [Correct Ans]: Much blurrier, , [Prog]: 568:  57%|▌| 568/999 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. Much blurrier\nC. Much 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 clear is the second image?
A. Similar
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 clear is the second image?
A. Similar
B. Much clearer
C. Much blurrier
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how clear is the second image?\nA. Similar\nB. Much clearer\nC. Much blurrier\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([-31.2812], 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]: 0.5581,[Response]: B.<|endoftext|>, [Correct Ans]: Much blurrier, , [Prog]: 568:  57%|▌| 569/999 [
[Running Accuracy]: 0.5571,[Response]: C.<|endoftext|>, [Correct Ans]: Much clearer, , [Prog]: 569:  57%|▌| 569/999 [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. Similar\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>Compared to the first image, how is the composition of the second image?
A. Much better composition
B. Much worse composition
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. Much better composition
B. Much worse composition
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. Much better composition\nB. Much worse composition\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([-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]: 0.5571,[Response]: C.<|endoftext|>, [Correct Ans]: Much clearer, , [Prog]: 569:  57%|▌| 570/999 [0
[Running Accuracy]: 0.5579,[Response]: A.<|endoftext|>, [Correct Ans]: Much better composition, , [Prog]: 570:  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 first image, how is the composition of the second image?\nA. Much better composition\nB. Much worse composition\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 true is the second image?
A. Much worse
B. Much 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 first image, how true is the second image?
A. Much worse
B. Much more real
C. About the same
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how true is the second image?\nA. Much worse\nB. Much more real\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([-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]: 0.5579,[Response]: A.<|endoftext|>, [Correct Ans]: Much better composition, , [Prog]: 570:  57%|▌|
[Running Accuracy]: 0.5569,[Response]: C.<|endoftext|>, [Correct Ans]: Much worse, , [Prog]: 571:  57%|▌| 571/999 [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 true is the second image?\nA. Much worse\nB. Much more real\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 part below is not affected by noise?
A. Rabbit in the first image
B. Figure in the second image
C. Giraffe 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. Rabbit in the first image
B. Figure in the second image
C. Giraffe 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. Rabbit in the first image\nB. Figure in the second image\nC. Giraffe 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([-30.4062], 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]: 0.5569,[Response]: C.<|endoftext|>, [Correct Ans]: Much worse, , [Prog]: 571:  57%|▌| 572/999 [06:
[Running Accuracy]: 0.5559,[Response]: B.<|endoftext|>, [Correct Ans]: Rabbit in the first image, , [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>Which part below is not affected by noise?\nA. Rabbit in the first image\nB. Figure in the second image\nC. Giraffe 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 affected by motion blur?
A. The child in the center of the first image
B. The lower right corner of the first image
C. The person in the second image
D. The motorcycle 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 child in the center of the first image
B. The lower right corner of the first image
C. The person in the second image
D. The motorcycle 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 child in the center of the first image\nB. The lower right corner of the first image\nC. The person in the second image\nD. The motorcycle in the second image\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])
 C.

[Running Accuracy]: 0.5559,[Response]: B.<|endoftext|>, [Correct Ans]: Rabbit in the first image, , [Prog]: 572:  57%|
[Running Accuracy]: 0.5550,[Response]: C.<|endoftext|>, [Correct Ans]: The lower right corner of the first 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 below is affected by motion blur?\nA. The child in the center of the first image\nB. The lower right corner of the first image\nC. The person in the second image\nD. The motorcycle 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 not affected by noise?
A. Background of the second image
B. Figure in the second image
C. Seesaw 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 noise?
A. Background of the second image
B. Figure in the second image
C. Seesaw in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is not affected by noise?\nA. Background of the second image\nB. Figure in the second image\nC. Seesaw 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([-31.2500], 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]: 0.5550,[Response]: C.<|endoftext|>, [Correct Ans]: The lower right corner of the first image, , [P
[Running Accuracy]: 0.5557,[Response]: C.<|endoftext|>, [Correct Ans]: Seesaw in the first image, , [Prog]: 574:  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>Which part below is not affected by noise?\nA. Background of the second image\nB. Figure in the second image\nC. Seesaw 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>What kind of distortion is not present 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:What kind of distortion is not present 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: [["What kind of distortion is not present 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([-31.2656], 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([-31.0156], 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]: 0.5557,[Response]: C.<|endoftext|>, [Correct Ans]: Seesaw in the first image, , [Prog]: 574:  58%|
[Running Accuracy]: 0.5565,[Response]: A.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 575:  58%|▌| 575/999 [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 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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 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 lower than 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 lower 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([-31.1406], 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]: 0.5565,[Response]: A.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 575:  58%|▌| 576/999 [0
[Running Accuracy]: 0.5573,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 576:  58%|▌| 576/999 [07:02<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 clarity 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 first image more affected by motion blur 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 affected by motion blur 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 affected by motion blur 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([-30.9688], 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]: 0.5573,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 576:  58%|▌| 577/999 [07:03<06:1
[Running Accuracy]: 0.5581,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 577:  58%|▌| 577/999 [07:03<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 affected by motion blur 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>Did both of these images have overexposure issues?
A. No
B. Yes
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. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Did both of these images have overexposure issues?\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([-31.0312], 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]: 0.5581,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 577:  58%|▌| 578/999 [07:04<06:2
[Running Accuracy]: 0.5588,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 578:  58%|▌| 578/999 [07:04<06:26
 {'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>Did both of these images have overexposure issues?\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 second image affected by motion blur?
A. Slighter
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 is the second image affected by motion blur?
A. Slighter
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 is the second image affected by motion blur?\nA. Slighter\nB. More severe\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([-30.8438], 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]: 0.5588,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 578:  58%|▌| 579/999 [07:05<06:26
[Running Accuracy]: 0.5596,[Response]: B.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 579:  58%|▌| 579/999 [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 second image affected by motion blur?\nA. Slighter\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>Neither of the two images below has any distortion?
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:Neither of the two images below has any distortion?
A. Out of focus
B. Noise
C. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["Neither of the two images below has any distortion?\nA. Out of focus\nB. Noise\nC. Overexposure\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([-31.1094], 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]: 0.5596,[Response]: B.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 579:  58%|▌| 580/999 [07
[Running Accuracy]: 0.5586,[Response]: C.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 580:  58%|▌| 580/999 [07:06<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>Neither of the two images below has any distortion?\nA. Out of focus\nB. Noise\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>Have both of these images encountered a focus issue?
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 encountered a focus issue?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Have both of these images encountered a focus issue?\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([-30.5938], 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]: 0.5586,[Response]: C.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 580:  58%|▌| 581/999 [07:07<06
[Running Accuracy]: 0.5577,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 581:  58%|▌| 581/999 [07:07<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>Have both of these images encountered a focus issue?\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 composition of the second image?
A. Worse
B. About the same
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 first image, how is the composition of the second image?
A. Worse
B. About the same
C. 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. Worse\nB. About the same\nC. Better\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([-31.5469], 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]: 0.5577,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 581:  58%|▌| 582/999 [07:07<06:11
[Running Accuracy]: 0.5584,[Response]: C.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 582:  58%|▌| 582/999 [07:07<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>Compared to the first image, how is the composition of the second image?\nA. Worse\nB. About the same\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 part below is affected by overexposure?
A. The right side of the sky in the first image
B. The pathway in the first image
C. The flowers in the second image
D. The green 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 below is affected by overexposure?
A. The right side of the sky in the first image
B. The pathway in the first image
C. The flowers in the second image
D. The green leaves in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is affected by overexposure?\nA. The right side of the sky in the first image\nB. The pathway in the first image\nC. The flowers in the second image\nD. The green leaves 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([-30.7188], 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]: 0.5584,[Response]: C.<|endoftext|>, [Correct Ans]: Better, , [Prog]: 582:  58%|▌| 583/999 [07:08<0
[Running Accuracy]: 0.5592,[Response]: A.<|endoftext|>, [Correct Ans]: The right side of the sky 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 affected by overexposure?\nA. The right side of the sky in the first image\nB. The pathway in the first image\nC. The flowers in the second image\nD. The green 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>Compared to the first image, how is the lighting in the second image?
A. More sufficient
B. Almost the same
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 lighting in the second image?
A. More sufficient
B. Almost the same
C. Worse
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. Almost the same\nC. 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([-31.2031], 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]: 0.5592,[Response]: A.<|endoftext|>, [Correct Ans]: The right side of the sky in the first image, ,
[Running Accuracy]: 0.5599,[Response]: C.<|endoftext|>, [Correct Ans]: Worse, , [Prog]: 584:  58%|▌| 584/999 [07:09<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>Compared to the first image, how is the lighting in the second image?\nA. More sufficient\nB. Almost the same\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 texture detail of the second image richer compared to 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 texture detail of the second image richer compared to the first image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the texture detail of the second image richer compared to 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([-31.3125], 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]: 0.5599,[Response]: C.<|endoftext|>, [Correct Ans]: Worse, , [Prog]: 584:  59%|▌| 585/999 [07:10<05
[Running Accuracy]: 0.5590,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 585:  59%|▌| 585/999 [07:10<05:54
 {'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 texture detail of the second image richer compared to 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>What kind of distortion did not occur in the following two images?
A. Motion blur
B. Noise
C. Compression distortion
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 the following two images?
A. Motion blur
B. Noise
C. Compression distortion
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion did not occur in the following two images?\nA. Motion blur\nB. Noise\nC. Compression distortion\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])
 C.

[Running Accuracy]: 0.5590,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 585:  59%|▌| 586/999 [07:11<06:08
[Running Accuracy]: 0.5580,[Response]: C.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 586:  59%|▌| 586/999 [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 the following two images?\nA. Motion blur\nB. Noise\nC. Compression distortion\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 much 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 much 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 much 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([-30.7188], 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]: 0.5580,[Response]: C.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 586:  59%|▌| 587/999 [07
[Running Accuracy]: 0.5571,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 587:  59%|▌| 587/999 [07:12<06:06
 {'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>Is the first image much 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 overexposure?
A. The cat 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 most affected by overexposure?
A. The cat 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 most affected by overexposure?\nA. The cat 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([-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([-31.2656], 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]: 0.5571,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 587:  59%|▌| 588/999 [07:13<06:36
[Running Accuracy]: 0.5578,[Response]: B.<|endoftext|>, [Correct Ans]: The background in the first image, , [Prog]: 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>Which part below is most affected by overexposure?\nA. The cat 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>What kind of distortion is not present in the following two images?
A. motion blur
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:What kind of distortion is not present in the following two images?
A. motion blur
B. out-of-focus
C. overexposure
D. underexposure
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion is not present in the following two images?\nA. motion blur\nB. out-of-focus\nC. overexposure\nD. underexposure\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([-31.0781], 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]: 0.5578,[Response]: B.<|endoftext|>, [Correct Ans]: The background in the first image, , [Prog]: 58
[Running Accuracy]: 0.5586,[Response]: D.<|endoftext|>, [Correct Ans]: underexposure, , [Prog]: 589:  59%|▌| 589/999 [
 {'prompt': "A chat between a curious user and an artificial 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 following two images?\nA. motion blur\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>Compared to the first image, how is the clarity of the second image?
A. Stronger
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 first image, how is the clarity of the second image?
A. Stronger
B. Slightly 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 clarity of the second image?\nA. Stronger\nB. Slightly worse\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([-31.4688], 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]: 0.5586,[Response]: D.<|endoftext|>, [Correct Ans]: underexposure, , [Prog]: 589:  59%|▌| 590/999 [
[Running Accuracy]: 0.5576,[Response]: C.<|endoftext|>, [Correct Ans]: Slightly worse, , [Prog]: 590:  59%|▌| 590/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. Stronger\nB. Slightly 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>What distortion problems are present in the following two images?
A. Underexposure
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 distortion problems are present in the following two images?
A. Underexposure
B. Motion blur
C. Overexposure
D. Noise
Answer with the option's letter from the given choices directly.

prompts: [["What distortion problems are present in the following two images?\nA. Underexposure\nB. Motion blur\nC. Overexposure\nD. Noise\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([-30.8594], 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]: 0.5576,[Response]: C.<|endoftext|>, [Correct Ans]: Slightly worse, , [Prog]: 590:  59%|▌| 591/999 
[Running Accuracy]: 0.5584,[Response]: A.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 591:  59%|▌| 591/999 [
 {'prompt': "A chat between a curious user and an artificial 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 problems are present in the following two images?\nA. Underexposure\nB. Motion blur\nC. Overexposure\nD. 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 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([-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]: Underexposure, , [Prog]: 591:  59%|▌| 592/999 [
[Running Accuracy]: 0.5591,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 592:  59%|▌| 592/999 [07:17<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 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 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([-31.1719], 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([-31.3438], 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]: 0.5591,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 592:  59%|▌| 593/999 [07:17<05:3
[Running Accuracy]: 0.5582,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 593:  59%|▌| 593/999 [07:17<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 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 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([-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([-30.7344], 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]: 0.5582,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 593:  59%|▌| 594/999 [07:18<05:16
[Running Accuracy]: 0.5572,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 594:  59%|▌| 594/999 [07:18<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>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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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([-31.2500], 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])
 A.

[Running Accuracy]: 0.5572,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 594:  60%|▌| 595/999 [07:19<05:30
[Running Accuracy]: 0.5563,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 595:  60%|▌| 595/999 [07:19<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 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>Which part below has not been affected by overexposure?
A. The trees in the first image
B. The leaves 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 below has not been affected by overexposure?
A. The trees in the first image
B. The leaves 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 below has not been affected by overexposure?\nA. The trees in the first image\nB. The leaves 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([-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([-31.0625], 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]: 0.5563,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 595:  60%|▌| 596/999 [07:20<05:0
[Running Accuracy]: 0.5570,[Response]: B.<|endoftext|>, [Correct Ans]: The leaves in the second image, , [Prog]: 596: 
 {'prompt': "A chat between a curious user and an artificial 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 trees in the first image\nB. The leaves 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 both of these images?
A. Underexposure
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:Which kind of distortion issue is not present in both of these images?
A. Underexposure
B. Noise
C. Motion blur
D. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["Which kind of distortion issue is not present in both of these images?\nA. Underexposure\nB. Noise\nC. Motion blur\nD. Overexposure\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])
 A.

[Running Accuracy]: 0.5570,[Response]: B.<|endoftext|>, [Correct Ans]: The leaves in the second image, , [Prog]: 596: 
[Running Accuracy]: 0.5561,[Response]: A.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 597:  60%|▌| 597/999 [07:20<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 kind of distortion issue is not present in both of these images?\nA. Underexposure\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 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([-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([-31.2500], 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]: 0.5561,[Response]: A.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 597:  60%|▌| 598/999 [07:21<04
[Running Accuracy]: 0.5552,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 598:  60%|▌| 598/999 [07:21<04:47
 {'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 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>What distortion is present in both of these 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 distortion is present in both of these images?
A. overexposure
B. noise
C. motion blur
Answer with the option's letter from the given choices directly.

prompts: [["What distortion is present in both of these images?\nA. overexposure\nB. noise\nC. motion blur\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([-30.8750], 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]: 0.5552,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 598:  60%|▌| 599/999 [07:22<04:38
[Running Accuracy]: 0.5559,[Response]: C.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 599:  60%|▌| 599/999 [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 distortion is present in both of these images?\nA. overexposure\nB. noise\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 first image, how's 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's 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's 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([-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([-31.2344], 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]: 0.5559,[Response]: C.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 599:  60%|▌| 600/999 [07
[Running Accuracy]: 0.5567,[Response]: C.<|endoftext|>, [Correct Ans]: Less vivid, , [Prog]: 600:  60%|▌| 600/999 [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's 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': '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([-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([-31.2031], 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]: 0.5567,[Response]: C.<|endoftext|>, [Correct Ans]: Less vivid, , [Prog]: 600:  60%|▌| 601/999 [07:
[Running Accuracy]: 0.5557,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 601:  60%|▌| 601/999 [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. 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 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([-30.8281], 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([-31.2500], 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]: 0.5557,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 601:  60%|▌| 602/999 [07
[Running Accuracy]: 0.5548,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 602:  60%|▌| 602/999 [07:24<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 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([-30.6719], 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([-30.9375], 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]: 0.5548,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 602:  60%|▌| 603/999 [07:24<04:3
[Running Accuracy]: 0.5539,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 603:  60%|▌| 603/999 [07:24<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>Which part is most affected by noise?
A. The bear in the second image
B. The store sign 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 is most affected by noise?
A. The bear in the second image
B. The store sign 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 is most affected by noise?\nA. The bear in the second image\nB. The store sign 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([-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([-30.7500], 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]: 0.5539,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 603:  60%|▌| 604/999 [07:25<04:2
[Running Accuracy]: 0.5546,[Response]: A.<|endoftext|>, [Correct Ans]: The bear in the second image, , [Prog]: 604:  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 affected by noise?\nA. The bear in the second image\nB. The store sign 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>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([-31.1094], 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([-30.5469], 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]: 0.5546,[Response]: A.<|endoftext|>, [Correct Ans]: The bear in the second image, , [Prog]: 604:  6
[Running Accuracy]: 0.5537,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 605:  61%|▌| 605/999 [07:26<04:22
 {'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>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>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([-31.0625], 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([-31.2969], 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]: 0.5537,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 605:  61%|▌| 606/999 [07:26<04:15
[Running Accuracy]: 0.5528,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 606:  61%|▌| 606/999 [07:26<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. 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 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([-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([-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]: 0.5528,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 606:  61%|▌| 607/999 [07:27<04:1
[Running Accuracy]: 0.5535,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 607:  61%|▌| 607/999 [07:27<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 is most severely affected by motion blur?
A. Left vehicle in the second image
B. Pedestrian in the first image
C. Street light in the first image
D. Right 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 is most severely affected by motion blur?
A. Left vehicle in the second image
B. Pedestrian in the first image
C. Street light in the first image
D. Right vehicle 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. Left vehicle in the second image\nB. Pedestrian in the first image\nC. Street light in the first image\nD. Right vehicle in the second 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([-30.9375], 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]: 0.5535,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 607:  61%|▌| 608/999 [07:28<04:1
[Running Accuracy]: 0.5543,[Response]: A.<|endoftext|>, [Correct Ans]: Left vehicle in the second image, , [Prog]: 608
 {'prompt': "A chat between a curious user and an artificial 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. Left vehicle in the second image\nB. Pedestrian in the first image\nC. Street light in the first image\nD. Right vehicle 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([-30.7188], 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([-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]: 0.5543,[Response]: A.<|endoftext|>, [Correct Ans]: Left vehicle in the second image, , [Prog]: 608
[Running Accuracy]: 0.5534,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 609:  61%|▌| 609/999 [07:28<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 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 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([-31.0781], 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([-31.4531], 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]: 0.5534,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 609:  61%|▌| 610/999 [07:29<04:5
[Running Accuracy]: 0.5525,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 610:  61%|▌| 610/999 [07:29<04:58
 {'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 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 overexposure?
A. Clouds in the sky in the first image
B. Leaves in the second image
C. Vehicles in the first image
D. Sun in 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. Clouds in the sky in the first image
B. Leaves in the second image
C. Vehicles in the first image
D. Sun in 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. Clouds in the sky in the first image\nB. Leaves in the second image\nC. Vehicles in the first image\nD. Sun in the sky 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([-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]: 0.5525,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 610:  61%|▌| 611/999 [07:30<04:44
[Running Accuracy]: 0.5532,[Response]: D.<|endoftext|>, [Correct Ans]: Sun in the sky 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 below is most severely affected by overexposure?\nA. Clouds in the sky in the first image\nB. Leaves in the second image\nC. Vehicles in the first image\nD. Sun in the 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>Is the first image more aesthetically pleasing 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 aesthetically pleasing 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 aesthetically pleasing 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([-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]: 0.5532,[Response]: D.<|endoftext|>, [Correct Ans]: Sun in the sky in the second image, , [Prog]: 6
[Running Accuracy]: 0.5539,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 612:  61%|▌| 612/999 [07:31<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 aesthetically pleasing 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>Relative to the first image, how clear is the second 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:Relative to the first image, how clear is the second image?
A. Much clearer
B. About the same
C. Much blurrier
Answer with the option's letter from the given choices directly.

prompts: [["Relative to the first image, how clear is the second 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([-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([-30.8438], 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]: 0.5539,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 612:  61%|▌| 613/999 [07:31<04:3
[Running Accuracy]: 0.5546,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 613:  61%|▌| 613/999 
 {'prompt': "A chat between a curious user and an artificial 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. Much clearer\nB. About the same\nC. Much 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>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([-31.0156], 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])
 B.

[Running Accuracy]: 0.5546,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 613:  61%|▌| 614/999 
[Running Accuracy]: 0.5554,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 614:  61%|▌| 614/999 [07:32<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 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>Which part below is most affected by noise?
A. The characters in the first image
B. The sky in the second image
C. The wall 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 characters in the first image
B. The sky in the second image
C. The wall 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 characters in the first image\nB. The sky in the second image\nC. The wall 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([-31.0625], 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]: 0.5554,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 614:  62%|▌| 615/999 [07:33<04:2
[Running Accuracy]: 0.5561,[Response]: B.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 615:  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 noise?\nA. The characters in the first image\nB. The sky in the second image\nC. The wall 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 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([-31.1719], 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]: 0.5561,[Response]: B.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 615:  62
[Running Accuracy]: 0.5552,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 616:  62%|▌| 616/999 [07:33<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 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([-30.9375], 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([-31.2500], 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]: 0.5552,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 616:  62%|▌| 617/999 [07:34<04:1
[Running Accuracy]: 0.5559,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 617:  62%|▌| 617/999 [07:34<04:11
 {'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 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 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([-31.4375], 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([-30.8750], 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]: 0.5559,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 617:  62%|▌| 618/999 [07:35<04:14
[Running Accuracy]: 0.5566,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 618:  62%|▌| 618/999 [07:35<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. 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. Similar
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. Similar
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. Similar\nC. More sufficient\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([-30.8281], 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]: 0.5566,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 618:  62%|▌| 619/999 [07:35<04:1
[Running Accuracy]: 0.5574,[Response]: A.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 619:  62%|▌| 619/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. Similar\nC. More sufficient\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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([-30.9375], 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])
 B.

[Running Accuracy]: 0.5574,[Response]: A.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 619:  62%|▌| 620/999
[Running Accuracy]: 0.5581,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 620:  62%|▌| 620/999 [07:37<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 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>What kind of distortion is not present in these two images?
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:What kind of distortion is not present in these two images?
A. Motion blur
B. Overexposure
C. Noise
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. Noise\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([-31.2656], 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]: 0.5581,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 620:  62%|▌| 621/999 [07:38<05:2
[Running Accuracy]: 0.5572,[Response]: B.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 621:  62%|▌| 621/999 [07:38<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 is not present in these two images?\nA. Motion blur\nB. Overexposure\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>Did both of these images have the problem of insufficient lighting?
A. No
B. Yes
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 the problem of insufficient lighting?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Did both of these images have the problem of insufficient lighting?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
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])
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]: 0.5572,[Response]: B.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 621:  62%|▌| 622/999 [07:38<04
[Running Accuracy]: 0.5579,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 622:  62%|▌| 622/999 [07:38<04:57
 {'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>Did both of these images have the problem of insufficient lighting?\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 lighting in the second image?
A. More sufficient
B. Similar
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. More sufficient
B. Similar
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. More sufficient\nB. Similar\nC. Less sufficient\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([-31.5156], 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]: 0.5579,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 622:  62%|▌| 623/999 [07:39<04:48
[Running Accuracy]: 0.5586,[Response]: A.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 623:  62%|▌| 623/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. Similar\nC. Less sufficient\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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([-30.5312], 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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.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]: More sufficient, , [Prog]: 623:  62%|▌| 624/999
[Running Accuracy]: 0.5577,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 624:  62%|▌| 624/999 [07:39<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 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. Ground in the first image
B. Sky in the second 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. Ground in the first image
B. Sky in the second 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. Ground in the first image\nB. Sky in the second image\nC. Grassland 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([-30.9531], 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]: 0.5577,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 624:  63%|▋| 625/999 [07:40<04:2
[Running Accuracy]: 0.5584,[Response]: B.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 625:  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. Ground in the first image\nB. Sky in the second image\nC. Grassland 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 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([-30.8906], 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]: 0.5584,[Response]: B.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 625:  63%|▋|
[Running Accuracy]: 0.5575,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 626:  63%|▋| 626/999 [07:41<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>Which part below is most severely affected by overexposure?
A. The grass in the second image
B. The ground in the first image
C. The sky in the second image
D. 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 grass in the second image
B. The ground in the first image
C. The sky in the second image
D. 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 grass in the second image\nB. The ground in the first image\nC. The sky in the second image\nD. The person 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([-31.0938], 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]: 0.5575,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 626:  63%|▋| 627/999 [07:42<04:3
[Running Accuracy]: 0.5582,[Response]: C.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 627:  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. The grass in the second image\nB. The ground in the first image\nC. The sky in the second image\nD. 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 type of distortion is not affecting both of these images?
A. Overexposure
B. Noise
C. Motion blur
D. Ghosting
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 not affecting both of these images?
A. Overexposure
B. Noise
C. Motion blur
D. Ghosting
Answer with the option's letter from the given choices directly.

prompts: [["Which type of distortion is not affecting both of these images?\nA. Overexposure\nB. Noise\nC. Motion blur\nD. Ghosting\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([-30.9844], 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]: 0.5582,[Response]: C.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 627:  63
[Running Accuracy]: 0.5573,[Response]: A.<|endoftext|>, [Correct Ans]: Ghosting, , [Prog]: 628:  63%|▋| 628/999 [07:42
 {'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>Which type of distortion is not affecting both of these images?\nA. Overexposure\nB. Noise\nC. Motion blur\nD. 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>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([-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([-30.7031], 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])
 B.

[Running Accuracy]: 0.5573,[Response]: A.<|endoftext|>, [Correct Ans]: Ghosting, , [Prog]: 628:  63%|▋| 629/999 [07:43
[Running Accuracy]: 0.5580,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 629:  63%|▋| 629/999 [07:43<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 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>Are there certain amount of noise 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 certain amount of noise in both of these images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are there certain amount of noise in both of these images?\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([-30.9531], 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]: 0.5580,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 629:  63%|▋| 630/999 [07:44<04:1
[Running Accuracy]: 0.5571,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 630:  63%|▋| 630/999 [07:44<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 there certain amount of noise in both of these 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>Neither of the two images has any distortion?
A. Noise
B. Motion blur
C. Underexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Neither of the two images has any distortion?
A. Noise
B. Motion blur
C. Underexposure
Answer with the option's letter from the given choices directly.

prompts: [["Neither of the two images has any distortion?\nA. Noise\nB. Motion blur\nC. Underexposure\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([-30.3438], 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]: 0.5571,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 630:  63%|▋| 631/999 [07:44<04:1
[Running Accuracy]: 0.5563,[Response]: C.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 631:  63%|▋| 631/999 [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>Neither of the two images has any distortion?\nA. Noise\nB. Motion blur\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>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([-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([-30.9844], 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]: 0.5563,[Response]: C.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 631:  63%|▋| 632/999 [07
[Running Accuracy]: 0.5554,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 632:  63%|▋| 632/999 [07:45<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>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>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([-31.2969], 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([-31.0469], 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]: 0.5554,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 632:  63%|▋| 633/999 [07:46<04:0
[Running Accuracy]: 0.5561,[Response]: A.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 633:  63%|▋| 633/999 
 {'prompt': "A chat between a curious user and an artificial 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>Which part below is most severely affected by overexposure?
A. The wall in the second image
B. The ground in the first image
C. The trees in the first image
D. 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 wall in the second image
B. The ground in the first image
C. The trees in the first image
D. 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 wall in the second image\nB. The ground in the first image\nC. The trees in the first image\nD. The sky in the second 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([-30.5156], 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]: 0.5561,[Response]: A.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 633:  63%|▋| 634/999 
[Running Accuracy]: 0.5552,[Response]: B.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 634:  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. The wall in the second image\nB. The ground in the first image\nC. The trees in the first image\nD. 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>Compared to the first image, how rich 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 rich 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 rich 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([-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([-30.9062], 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]: 0.5552,[Response]: B.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 634:  64
[Running Accuracy]: 0.5559,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 635:  64%|▋| 635/999 [07:47<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. 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>Which part below is most severely affected by the influence of defocusing?
A. The person in the first image
B. The background in the second image
C. The pants 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 the influence of defocusing?
A. The person in the first image
B. The background in the second image
C. The pants in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by the influence of defocusing?\nA. The person in the first image\nB. The background in the second image\nC. The pants 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([-30.9531], 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]: 0.5559,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 635:  64%|▋| 636/999 [07:48<0
[Running Accuracy]: 0.5550,[Response]: C.<|endoftext|>, [Correct Ans]: The person in the first image, , [Prog]: 636:  
 {'prompt': "A chat between a curious user and an artificial 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 the influence of defocusing?\nA. The person in the first image\nB. The background in the second image\nC. The pants 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 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([-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])
 B.

[Running Accuracy]: 0.5550,[Response]: C.<|endoftext|>, [Correct Ans]: The person in the first image, , [Prog]: 636:  
[Running Accuracy]: 0.5542,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 637:  64%|▋| 637/999 [07:48<04:02
 {'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 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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([-31.1406], 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([-30.5625], 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]: 0.5542,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 637:  64%|▋| 638/999 [07:49<04:03
[Running Accuracy]: 0.5549,[Response]: A.<|endoftext|>, [Correct Ans]: Less real, , [Prog]: 638:  64%|▋| 638/999 [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>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>Compared to the first image, how rich is the color in the second image?
A. Richer
B. Almost 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. Almost 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. Almost the same\nC. More monotonous\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]: 0.5549,[Response]: A.<|endoftext|>, [Correct Ans]: Less real, , [Prog]: 638:  64%|▋| 639/999 [07:5
[Running Accuracy]: 0.5556,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 639:  64%|▋| 639/999 [07:50<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. Almost 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 clear is 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:Compared to the first image, how clear is the second image?
A. Clearer
B. More blurry
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. More blurry\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([-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]: 0.5556,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 639:  64%|▋| 640/999 [07:50<0
[Running Accuracy]: 0.5547,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 640:  64%|▋| 640/999 [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. Clearer\nB. More blurry\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 has the strongest light?
A. The wall of the first image
B. The window of the first image
C. The ceiling of the second image
D. The light fixture 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 strongest light?
A. The wall of the first image
B. The window of the first image
C. The ceiling of the second image
D. The light fixture of the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the strongest light?\nA. The wall of the first image\nB. The window of the first image\nC. The ceiling of the second image\nD. The light fixture of 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([-30.9531], 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])
 D.

[Running Accuracy]: 0.5547,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 640:  64%|▋| 641/999 [07
[Running Accuracy]: 0.5538,[Response]: D.<|endoftext|>, [Correct Ans]: The window of the first image, , [Prog]: 641:  
 {'prompt': "A chat between a curious user and an artificial 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 strongest light?\nA. The wall of the first image\nB. The window of the first image\nC. The ceiling of the second image\nD. The light fixture of 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 color richness of the second image?
A. Similar
B. More monotonous
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. More monotonous
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. More monotonous\nC. Richer\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([-31.3125], 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]: 0.5538,[Response]: D.<|endoftext|>, [Correct Ans]: The window of the first image, , [Prog]: 641:  
[Running Accuracy]: 0.5530,[Response]: C.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 642:  64%|▋| 642/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. More monotonous\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 part below is most severely affected by noise?
A. Ground in the second image
B. Sky in the first image
C. Stone wall 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 part below is most severely affected by noise?
A. Ground in the second image
B. Sky in the first image
C. Stone wall in the first image
D. 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. Ground in the second image\nB. Sky in the first image\nC. Stone wall in the first image\nD. Sky 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([-31.0781], 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]: 0.5530,[Response]: C.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 642:  64%|▋| 643/999
[Running Accuracy]: 0.5521,[Response]: A.<|endoftext|>, [Correct Ans]: Stone wall in the first image, , [Prog]: 643:  
 {'prompt': "A chat between a curious user and an artificial 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. Ground in the second image\nB. Sky in the first image\nC. Stone wall in the first image\nD. 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 sharpness of the second image?
A. Much blurrier
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 is the sharpness of the second image?
A. Much blurrier
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 is the sharpness of the second image?\nA. Much blurrier\nB. About the same\nC. Much 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([-31.3750], 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]: 0.5521,[Response]: A.<|endoftext|>, [Correct Ans]: Stone wall in the first image, , [Prog]: 643:  
[Running Accuracy]: 0.5528,[Response]: A.<|endoftext|>, [Correct Ans]: Much blurrier, , [Prog]: 644:  64%|▋| 644/999 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 blurrier\nB. About the same\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>What are the distortion issues in these two images?
A. Motion blur
B. Lens flare
C. Overexposure
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:What are the distortion issues in these two images?
A. Motion blur
B. Lens flare
C. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["What are the distortion issues in these two images?\nA. Motion blur\nB. Lens flare\nC. Overexposure\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([-30.3438], 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]: 0.5528,[Response]: A.<|endoftext|>, [Correct Ans]: Much blurrier, , [Prog]: 644:  65%|▋| 645/999 [
[Running Accuracy]: 0.5535,[Response]: C.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 645:  65%|▋| 645/999 [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 are the distortion issues in these two images?\nA. Motion blur\nB. Lens flare\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 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([-31.8750], 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([-31.0312], 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]: 0.5535,[Response]: C.<|endoftext|>, [Correct Ans]: Overexposure, , [Prog]: 645:  65%|▋| 646/999 [0
[Running Accuracy]: 0.5526,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 646:  65%|▋| 646/999 [07:54<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>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>Compared to the first image, how would you rate the richness of colors in the second image?
A. Richer
B. Almost 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 would you rate the richness of colors in the second image?
A. Richer
B. Almost the same
C. More monotonous
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how would you rate the richness of colors in the second image?\nA. Richer\nB. Almost the same\nC. More monotonous\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([-31.1875], 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]: 0.5526,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 646:  65%|▋| 647/999 [07:55<04:21
[Running Accuracy]: 0.5533,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 647:  65%|▋| 647/999 [07:55<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 would you rate the richness of colors in the second image?\nA. Richer\nB. Almost 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>Are the texture details of these two images both relatively 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 relatively 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 relatively rich?\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([-30.6719], 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]: 0.5533,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 647:  65%|▋| 648/999 [07:56<0
[Running Accuracy]: 0.5540,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 648:  65%|▋| 648/999 [07:56<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>Are the texture details of these two images both relatively rich?\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. 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([-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([-31.3750], 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]: 0.5540,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 648:  65%|▋| 649/999 [07:57<04:2
[Running Accuracy]: 0.5532,[Response]: B.<|endoftext|>, [Correct Ans]: Less vivid, , [Prog]: 649:  65%|▋| 649/999 [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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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([-31.2500], 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([-31.5469], 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]: 0.5532,[Response]: B.<|endoftext|>, [Correct Ans]: Less vivid, , [Prog]: 649:  65%|▋| 650/999 [07:
[Running Accuracy]: 0.5523,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 650:  65%|▋| 650/999 [07:57<04:13
 {'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. 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 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([-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([-31.3281], 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]: 0.5523,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 650:  65%|▋| 651/999 [07:58<04:13
[Running Accuracy]: 0.5515,[Response]: C.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 651:  65%|▋| 651/999 [07:58<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 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': '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>Do both of these images have motion blur issues?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Do both of these images have motion blur issues?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Do both of these images have motion blur issues?\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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.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]: C.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 651:  65%|▋| 652/999 [07:59<0
[Running Accuracy]: 0.5521,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 652:  65%|▋| 652/999 [07:59<04:05
 {'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>Do both of these images have motion blur issues?\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 is the lighting of the second image compared to the first image?
A. less adequate
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:How is the lighting of the second image compared to the first image?
A. less adequate
B. more adequate
C. about the same
Answer with the option's letter from the given choices directly.

prompts: [["How is the lighting of the second image compared to the first image?\nA. less adequate\nB. more adequate\nC. about the same\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([-31.1094], 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]: 0.5521,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 652:  65%|▋| 653/999 [07:59<03:59
[Running Accuracy]: 0.5528,[Response]: B.<|endoftext|>, [Correct Ans]: more adequate, , [Prog]: 653:  65%|▋| 653/999 [
 {'prompt': "A chat between a curious user and an artificial 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 lighting of the second image compared to the first image?\nA. less adequate\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>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.], device='cuda:0', dtype=torch.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]: 0.5528,[Response]: B.<|endoftext|>, [Correct Ans]: more adequate, , [Prog]: 653:  65%|▋| 654/999 [
[Running Accuracy]: 0.5520,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 654:  65%|▋| 654/999 [08:00<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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 affected by overexposure?
A. The airplane in the second image
B. The wall in the first image
C. The light source in the second image
D. The ornament 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 affected by overexposure?
A. The airplane in the second image
B. The wall in the first image
C. The light source in the second image
D. The ornament in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below has been affected by overexposure?\nA. The airplane in the second image\nB. The wall in the first image\nC. The light source in the second image\nD. The ornament 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([-31.3281], 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]: 0.5520,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 654:  66%|▋| 655/999 [08:01<04:1
[Running Accuracy]: 0.5527,[Response]: C.<|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 below has been affected by overexposure?\nA. The airplane in the second image\nB. The wall in the first image\nC. The light source in the second image\nD. The ornament 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 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([-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])
 B.

[Running Accuracy]: 0.5527,[Response]: C.<|endoftext|>, [Correct Ans]: The light source in the second image, , [Prog]:
[Running Accuracy]: 0.5518,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 656:  66%|▋| 656/999 [08:02<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 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>Which part below has been affected by overexposure?
A. The hair of the person in the first image
B. The face of the person in the first image
C. The dandelion 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 been affected by overexposure?
A. The hair of the person in the first image
B. The face of the person in the first image
C. The dandelion in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below has been affected by overexposure?\nA. The hair of the person in the first image\nB. The face of the person in the first image\nC. The dandelion in 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([-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])
 C.

[Running Accuracy]: 0.5518,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 656:  66%|▋| 657/999 [08:02<03:5
[Running Accuracy]: 0.5510,[Response]: C.<|endoftext|>, [Correct Ans]: The hair of the person in the first 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 below has been affected by overexposure?\nA. The hair of the person in the first image\nB. The face of the person in the first image\nC. The dandelion 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 affected by motion blur?
A. The bridge in the first image
B. The river surface in the first image
C. The red car in the second image
D. The streetlight 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 bridge in the first image
B. The river surface in the first image
C. The red car in the second image
D. The streetlight 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 bridge in the first image\nB. The river surface in the first image\nC. The red car in the second image\nD. The streetlight 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([-31.1094], 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]: 0.5510,[Response]: C.<|endoftext|>, [Correct Ans]: The hair of the person in the first image, , [P
[Running Accuracy]: 0.5517,[Response]: C.<|endoftext|>, [Correct Ans]: The red car in the second image, , [Prog]: 658:
 {'prompt': "A chat between a curious user and an artificial 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 bridge in the first image\nB. The river surface in the first image\nC. The red car in the second image\nD. The streetlight 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, what is the level of noise in the second image?
A. Similar
B. More severe
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, what is the level of noise in the second image?
A. Similar
B. More severe
C. Slighter
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. More severe\nC. Slighter\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([-30.8594], 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]: 0.5517,[Response]: C.<|endoftext|>, [Correct Ans]: The red car in the second image, , [Prog]: 658:
[Running Accuracy]: 0.5524,[Response]: B.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 659:  66%|▋| 659/999 [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, what is the level of noise in the second image?\nA. Similar\nB. More severe\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>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([-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])
 A.

[Running Accuracy]: 0.5524,[Response]: B.<|endoftext|>, [Correct Ans]: More severe, , [Prog]: 659:  66%|▋| 660/999 [08
[Running Accuracy]: 0.5530,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 660:  66%|▋| 660/999 [08:04<03:46
 {'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>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>Are the illumination of these two images both weak?
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 illumination of these two images both weak?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the illumination of these two images both weak?\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([-31.2188], 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]: 0.5530,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 660:  66%|▋| 661/999 [08:05<03:40
[Running Accuracy]: 0.5537,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 661:  66%|▋| 661/999 [08:05<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>Are the illumination of these two images both weak?\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>No distortion appears in these two images?
A. motion blur
B. noise
C. overexposure
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. motion blur
B. noise
C. overexposure
Answer with the option's letter from the given choices directly.

prompts: [["No distortion appears in these two images?\nA. motion blur\nB. noise\nC. overexposure\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([-31.1719], 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]: 0.5537,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 661:  66%|▋| 662/999 [08:06<03:3
[Running Accuracy]: 0.5529,[Response]: B.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 662:  66%|▋| 662/999 [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>No distortion appears in these two images?\nA. motion blur\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>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([-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([-31.3125], 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]: 0.5529,[Response]: B.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 662:  66%|▋| 663/999 [08
[Running Accuracy]: 0.5535,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 663:  66%|▋| 663/999 [08:06<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 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>What kind of distortion did not appear 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:What kind of distortion did not appear in these two images?
A. Noise
B. Overexposure
C. Motion blur
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion did not appear 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([-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([-31.2656], 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]: 0.5535,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 663:  66%|▋| 664/999 [08:07<03:3
[Running Accuracy]: 0.5527,[Response]: A.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 664:  66%|▋| 664/999 [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 did not appear 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 trees in the second image
C. The vehicles 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 trees in the second image
C. The vehicles 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 trees in the second image\nC. The vehicles 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([-31.0156], 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]: 0.5527,[Response]: A.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 664:  67%|▋| 665/999 [08
[Running Accuracy]: 0.5519,[Response]: A.<|endoftext|>, [Correct Ans]: The trees in the second image, , [Prog]: 665:  
 {'prompt': "A chat between a curious user and an artificial 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 trees in the second image\nC. The vehicles 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. 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([-30.9062], 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([-30.2812], 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]: 0.5519,[Response]: A.<|endoftext|>, [Correct Ans]: The trees in the second image, , [Prog]: 665:  
[Running Accuracy]: 0.5526,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 666:  67%|▋| 666/999 [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 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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 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 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 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([-30.7812], 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]: 0.5526,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 666:  67%|▋| 667/999 [08
[Running Accuracy]: 0.5517,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 667:  67%|▋| 667/999 [08:09<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 sharpness of the first image higher 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 insufficient 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 lighting insufficient in both of these images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the lighting insufficient in both of these images?\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([-31.2500], 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]: 0.5517,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 667:  67%|▋| 668/999 [08:09<03:3
[Running Accuracy]: 0.5524,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 668:  67%|▋| 668/999 [08:09<03:35
 {'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 lighting insufficient in both of these 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 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([-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])
 B.

[Running Accuracy]: 0.5524,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 668:  67%|▋| 669/999 [08:10<03:36
[Running Accuracy]: 0.5516,[Response]: B.<|endoftext|>, [Correct Ans]: Similar, , [Prog]: 669:  67%|▋| 669/999 [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 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>Which part below is most severely affected by overexposure?
A. The figures in the second image
B. The figures in the first 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 overexposure?
A. The figures in the second image
B. The figures in the first 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 overexposure?\nA. The figures in the second image\nB. The figures in the first image\nC. The 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([-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]: 0.5516,[Response]: B.<|endoftext|>, [Correct Ans]: Similar, , [Prog]: 669:  67%|▋| 670/999 [08:11<
[Running Accuracy]: 0.5522,[Response]: C.<|endoftext|>, [Correct Ans]: The sky in the first image, , [Prog]: 670:  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 most severely affected by overexposure?\nA. The figures in the second image\nB. The figures in the first image\nC. The 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>What kind of distortion is present in both of these images?
A. motion blur
B. out-of-focus
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 kind of distortion is present in both of these images?
A. motion blur
B. out-of-focus
C. noise
D. overexposure
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion is present in both of these images?\nA. motion blur\nB. out-of-focus\nC. noise\nD. overexposure\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])
 D.

[Running Accuracy]: 0.5522,[Response]: C.<|endoftext|>, [Correct Ans]: The sky in the first image, , [Prog]: 670:  67%
[Running Accuracy]: 0.5529,[Response]: D.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 671:  67%|▋| 671/999 [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 present in both of these images?\nA. motion blur\nB. out-of-focus\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>Which part below is most severely affected by overexposure?
A. Coral in the first image
B. Left wall in the second image
C. Teddy bear toy 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. Coral in the first image
B. Left wall in the second image
C. Teddy bear toy 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. Coral in the first image\nB. Left wall in the second image\nC. Teddy bear toy 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([-30.8594], 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]: 0.5529,[Response]: D.<|endoftext|>, [Correct Ans]: overexposure, , [Prog]: 671:  67%|▋| 672/999 [0
[Running Accuracy]: 0.5536,[Response]: B.<|endoftext|>, [Correct Ans]: Left wall in the second image, , [Prog]: 672:  
 {'prompt': "A chat between a curious user and an artificial 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. Coral in the first image\nB. Left wall in the second image\nC. Teddy bear toy 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 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 images overexposed?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both images overexposed?\nA. No\nB. Yes\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([-31.0781], 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]: 0.5536,[Response]: B.<|endoftext|>, [Correct Ans]: Left wall in the second image, , [Prog]: 672:  
[Running Accuracy]: 0.5542,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 673:  67%|▋| 673/999 [08:13<03:35
 {'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>Are both 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 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([-30.6875], 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([-31.1875], 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]: 0.5542,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 673:  67%|▋| 674/999 [08:13<03:32
[Running Accuracy]: 0.5549,[Response]: B.<|endoftext|>, [Correct Ans]: less rich, , [Prog]: 674:  67%|▋| 674/999 [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 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>How does the illumination 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 illumination 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 illumination 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([-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([-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]: 0.5549,[Response]: B.<|endoftext|>, [Correct Ans]: less rich, , [Prog]: 674:  68%|▋| 675/999 [08:1
[Running Accuracy]: 0.5556,[Response]: C.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 675:  68%|▋| 675/999
 {'prompt': "A chat between a curious user and an artificial 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. 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>Which part below is most severely affected by motion blur?
A. The road surface in the first image
B. The potted plant in the second image
C. The pedestrian 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 road surface in the first image
B. The potted plant in the second image
C. The pedestrian 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 road surface in the first image\nB. The potted plant in the second image\nC. The pedestrian 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([-30.6250], 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]: 0.5556,[Response]: C.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 675:  68%|▋| 676/999
[Running Accuracy]: 0.5562,[Response]: B.<|endoftext|>, [Correct Ans]: The potted plant 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 motion blur?\nA. The road surface in the first image\nB. The potted plant in the second image\nC. The pedestrian 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 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([-30.8438], 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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.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]: The potted plant in the second image, , [Prog]:
[Running Accuracy]: 0.5569,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 677:  68%|▋| 677/999 [08:15<03:30
 {'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 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 real is the second image?
A. More real
B. About the same
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. More real
B. About the same
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. More real\nB. About the same\nC. Less real\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([-31.1406], 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]: 0.5569,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 677:  68%|▋| 678/999 [08:16<03:28
[Running Accuracy]: 0.5575,[Response]: C.<|endoftext|>, [Correct Ans]: Less real, , [Prog]: 678:  68%|▋| 678/999 [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 real is the second image?\nA. More real\nB. About the same\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>Compared to the first image, how would you rate the richness of colors in the second image?
A. More monotonous
B. 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 would you rate the richness of colors in the second image?
A. More monotonous
B. Richer
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 richness of colors in the second image?\nA. More monotonous\nB. Richer\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([-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]: C.<|endoftext|>, [Correct Ans]: Less real, , [Prog]: 678:  68%|▋| 679/999 [08:1
[Running Accuracy]: 0.5582,[Response]: A.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 679:  68%|▋| 679/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 richness of colors in the second image?\nA. More monotonous\nB. 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>Compared to the first image, how real is 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 real is 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 real is 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([-30.0625], 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([-31.2969], 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]: 0.5582,[Response]: A.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 679:  68%|▋| 680/999
[Running Accuracy]: 0.5588,[Response]: B.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 680:  68%|▋| 680/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. 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>Which part below is most affected by motion blur?
A. The keyboard in the first image
B. The sky in the second image
C. The fireworks 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 keyboard in the first image
B. The sky in the second image
C. The fireworks 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 keyboard in the first image\nB. The sky in the second image\nC. The fireworks in the second image\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])
 C.

[Running Accuracy]: 0.5588,[Response]: B.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 680:  68%|▋| 681/999 
[Running Accuracy]: 0.5580,[Response]: C.<|endoftext|>, [Correct Ans]: The keyboard in the first image, , [Prog]: 681:
 {'prompt': "A chat between a curious user and an artificial 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 keyboard in the first image\nB. The sky in the second image\nC. The fireworks 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 the most unreal?
A. The temple in the first image
B. The figures 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 the most unreal?
A. The temple in the first image
B. The figures 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 the most unreal?\nA. The temple in the first image\nB. The figures 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([-30.8438], 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([-31.3906], 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]: 0.5580,[Response]: C.<|endoftext|>, [Correct Ans]: The keyboard in the first image, , [Prog]: 681:
[Running Accuracy]: 0.5587,[Response]: B.<|endoftext|>, [Correct Ans]: The figures in the second image, , [Prog]: 682:
 {'prompt': "A chat between a curious user and an artificial 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 most unreal?\nA. The temple in the first image\nB. The figures 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>Which part below is most severely affected by overexposure?
A. The wall in the first image
B. The figure in the first image
C. The display screen 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 wall in the first image
B. The figure in the first image
C. The display screen 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 wall in the first image\nB. The figure in the first image\nC. The display screen 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([-31.5156], 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]: 0.5587,[Response]: B.<|endoftext|>, [Correct Ans]: The figures in the second image, , [Prog]: 682:
[Running Accuracy]: 0.5593,[Response]: C.<|endoftext|>, [Correct Ans]: The display screen 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 wall in the first image\nB. The figure in the first image\nC. The display screen 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. 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([-31.2812], 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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.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]: The display screen in the second image, , [Prog
[Running Accuracy]: 0.5585,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 684:  68%|▋| 684/999 [08:20<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 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 details and textures 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:Are the details and textures 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: [["Are the details and textures 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([-30.7188], 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([-31.0625], 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]: 0.5585,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 684:  69%|▋| 685/999 [08:21<03:5
[Running Accuracy]: 0.5591,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 685:  69%|▋| 685/999 [08:21<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>Are the details and textures 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 noise?
A. The ground in the second image
B. The person in the second image
C. The 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 is not affected by noise?
A. The ground in the second image
B. The person in the second image
C. The leaves in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is not affected by noise?\nA. The ground in the second image\nB. The person in the second image\nC. The leaves in the 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([-31.2812], 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]: 0.5591,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 685:  69%|▋| 686/999 [08:22<03:4
[Running Accuracy]: 0.5583,[Response]: B.<|endoftext|>, [Correct Ans]: The leaves in the first image, , [Prog]: 686:  
 {'prompt': "A chat between a curious user and an artificial 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 ground in the second image\nB. The person in the second image\nC. The leaves 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([-31.2188], 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])
 A.

[Running Accuracy]: 0.5583,[Response]: B.<|endoftext|>, [Correct Ans]: The leaves in the first image, , [Prog]: 686:  
[Running Accuracy]: 0.5575,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 687:  69%|▋| 687/999 [08:22<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 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 free from 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:Are both of these images free from motion blur?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images free from motion blur?\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([-31.4219], 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]: 0.5575,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 687:  69%|▋| 688/999 [08:23<03:5
[Running Accuracy]: 0.5567,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 688:  69%|▋| 688/999 [08:23<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>Are both of these images free from motion blur?\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 experiencing 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 experiencing overexposure issues?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images experiencing overexposure issues?\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([-30.9688], 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]: 0.5567,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 688:  69%|▋| 689/999 [08:24<03:4
[Running Accuracy]: 0.5573,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 689:  69%|▋| 689/999 [08:24<03:46
 {'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>Are both of these images experiencing 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 clarity of the second 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 first image, how is the clarity of the second 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 first image, how is the clarity of the second 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([-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([-30.8125], 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]: 0.5573,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 689:  69%|▋| 690/999 [08:25<03:38
[Running Accuracy]: 0.5580,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 690:  69%|▋| 690/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 clearer\nB. About the same\nC. Much 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>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([-30.8438], 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([-31.1875], 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]: 0.5580,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 690:  69%|▋| 691/999 
[Running Accuracy]: 0.5572,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 691:  69%|▋| 691/999 [08:25<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 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 serious overexposure issue?
A. The grassland in the second image
B. The badge 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 has the most serious overexposure issue?
A. The grassland in the second image
B. The badge 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 has the most serious overexposure issue?\nA. The grassland in the second image\nB. The badge 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.], device='cuda:0', dtype=torch.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]: 0.5572,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 691:  69%|▋| 692/999 [08:26<03:5
[Running Accuracy]: 0.5578,[Response]: C.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 692:  69
 {'prompt': "A chat between a curious user and an artificial 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 serious overexposure issue?\nA. The grassland in the second image\nB. The badge 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>What kind of distortion issues exist in both of these images?
A. Out of Focus
B. Underexposure
C. Ghosting
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 issues exist in both of these images?
A. Out of Focus
B. Underexposure
C. Ghosting
D. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion issues exist in both of these images?\nA. Out of Focus\nB. Underexposure\nC. Ghosting\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([-30.9219], 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]: 0.5578,[Response]: C.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 692:  69
[Running Accuracy]: 0.5570,[Response]: C.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 693:  69%|▋| 693/999 [
 {'prompt': "A chat between a curious user and an artificial 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 issues exist in both of these images?\nA. Out of Focus\nB. Underexposure\nC. Ghosting\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>Which type of distortion is not present in these two images?
A. Noise
B. Underexposure
C. 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 is not present in these two images?
A. Noise
B. Underexposure
C. Motion blur
Answer with the option's letter from the given choices directly.

prompts: [["Which type of distortion is not present in these two images?\nA. Noise\nB. Underexposure\nC. Motion blur\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([-31.0156], 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]: 0.5570,[Response]: C.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 693:  69%|▋| 694/999 [
[Running Accuracy]: 0.5562,[Response]: B.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 694:  69%|▋| 694/999 [08:28<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>Which type of distortion is not present in these two images?\nA. Noise\nB. Underexposure\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 kind of distortion does not exist in these 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:Which kind of distortion does not exist in these two images?
A. underexposure
B. overexposure
C. noise
D. motion blur
Answer with the option's letter from the given choices directly.

prompts: [["Which kind of distortion does not exist in these 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([-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]: 0.5562,[Response]: B.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 694:  70%|▋| 695/999 [08:28<03
[Running Accuracy]: 0.5554,[Response]: A<|endoftext|>, [Correct Ans]: noise, , [Prog]: 695:  70%|▋| 695/999 [08:28<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>Which kind of distortion does not exist in these 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>Compared to the first image, how is the sharpness of the second 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:Compared to the first image, how is the sharpness of the second image?
A. Blurrier
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. Blurrier\nB. About the same\nC. Sharper\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([-30.5156], 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]: 0.5554,[Response]: A<|endoftext|>, [Correct Ans]: noise, , [Prog]: 695:  70%|▋| 696/999 [08:29<03:
[Running Accuracy]: 0.5546,[Response]: A.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 696:  70%|▋| 696/999 [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>Compared to the first image, how is the sharpness of the second 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>Which part below has the most severe overexposure issue?
A. Signboard in the second image
B. Wall in the second image
C. Streetlight 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 issue?
A. Signboard in the second image
B. Wall in the second image
C. Streetlight in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below has the most severe overexposure issue?\nA. Signboard in the second image\nB. Wall in the second image\nC. Streetlight in the first 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([-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]: 0.5546,[Response]: A.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 696:  70%|▋| 697/999 [08:29<
[Running Accuracy]: 0.5552,[Response]: C.<|endoftext|>, [Correct Ans]: Streetlight in the first image, , [Prog]: 697: 
 {'prompt': "A chat between a curious user and an artificial 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 issue?\nA. Signboard in the second image\nB. Wall in the second image\nC. Streetlight 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 motion blur?
A. The ground in the first image
B. The pedestrian in the second image
C. The wall 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 first image
B. The pedestrian in the second image
C. The wall 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 first image\nB. The pedestrian in the second image\nC. The wall in the first 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([-30.5938], 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]: 0.5552,[Response]: C.<|endoftext|>, [Correct Ans]: Streetlight in the first image, , [Prog]: 697: 
[Running Accuracy]: 0.5559,[Response]: B.<|endoftext|>, [Correct Ans]: The pedestrian 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 below is most severely affected by motion blur?\nA. The ground in the first image\nB. The pedestrian in the second image\nC. The wall 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 of 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 lighting of 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 lighting of 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([-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([-31.1875], 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]: 0.5559,[Response]: B.<|endoftext|>, [Correct Ans]: The pedestrian in the second image, , [Prog]: 6
[Running Accuracy]: 0.5551,[Response]: A.<|endoftext|>, [Correct Ans]: More Adequate, , [Prog]: 699:  70%|▋| 699/999 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 Adequate\nB. About the Same\nC. More Adequate\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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([-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])
 A.

[Running Accuracy]: 0.5551,[Response]: A.<|endoftext|>, [Correct Ans]: More Adequate, , [Prog]: 699:  70%|▋| 700/999 [
[Running Accuracy]: 0.5557,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 700:  70%|▋| 700/999 [08:31<03:13
 {'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 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 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([-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([-31.0469], 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]: 0.5557,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 700:  70%|▋| 701/999 [08:32<03:44
[Running Accuracy]: 0.5549,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 701:  70%|▋| 701/999 [08:32<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 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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. Sharper
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. Similar
B. Sharper
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. Similar\nB. Sharper\nC. Blurrier\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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 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]: 0.5549,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 701:  70%|▋| 702/999 [08:33<03:3
[Running Accuracy]: 0.5541,[Response]: C.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 702:  70%|▋| 702/999 [08:33<
 {'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 sharpness of the second image?\nA. Similar\nB. Sharper\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>Do both of these images have some overexposure issue?
A. No
B. Yes
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:Do both of these images have some overexposure issue?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Do both of these images have some overexposure issue?\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]: 0.5541,[Response]: C.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 702:  70%|▋| 703/999 [08:34<
[Running Accuracy]: 0.5548,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 703:  70%|▋| 703/999 [08:34<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>Do both of these images have some overexposure issue?\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 noise level in the second image?
A. Similar
B. More severe
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 level in the second image?
A. Similar
B. More severe
C. Slighter
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the noise level in the second image?\nA. Similar\nB. More severe\nC. Slighter\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([-30.9219], 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]: 0.5548,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 703:  70%|▋| 704/999 [08:35<03:4
[Running Accuracy]: 0.5540,[Response]: B.<|endoftext|>, [Correct Ans]: Slighter, , [Prog]: 704:  70%|▋| 704/999 [08:35
 {'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 noise level in the second image?\nA. Similar\nB. More severe\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>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([-31.1094], 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([-30.9531], 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]: 0.5540,[Response]: B.<|endoftext|>, [Correct Ans]: Slighter, , [Prog]: 704:  71%|▋| 705/999 [08:35
[Running Accuracy]: 0.5532,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 705:  71%|▋| 705/999 [08:35<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>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 would you rate the clarity of the second image?
A. almost 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 would you rate the clarity of the second image?
A. almost 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 would you rate the clarity of the second image?\nA. almost the same\nB. more blurry\nC. clearer\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([-30.9844], 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]: 0.5532,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 705:  71%|▋| 706/999 [08:36<03:36
[Running Accuracy]: 0.5538,[Response]: B.<|endoftext|>, [Correct Ans]: more blurry, , [Prog]: 706:  71%|▋| 706/999 [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 would you rate the clarity of the second image?\nA. almost 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>Which kind of distortion is not present in these two images?
A. Noise
B. Ghosting
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 is not present in these two images?
A. Noise
B. Ghosting
C. Overexposure
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. Ghosting\nC. Overexposure\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([-30.9375], 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]: 0.5538,[Response]: B.<|endoftext|>, [Correct Ans]: more blurry, , [Prog]: 706:  71%|▋| 707/999 [08
[Running Accuracy]: 0.5530,[Response]: C.<|endoftext|>, [Correct Ans]: Ghosting, , [Prog]: 707:  71%|▋| 707/999 [08:37
 {'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 kind of distortion is not present in these two images?\nA. Noise\nB. Ghosting\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 first image much 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 much 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 much clearer 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([-30.7812], 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]: 0.5530,[Response]: C.<|endoftext|>, [Correct Ans]: Ghosting, , [Prog]: 707:  71%|▋| 708/999 [08:38
[Running Accuracy]: 0.5537,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 708:  71%|▋| 708/999 [08:38<03:35
 {'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 much 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>How is the lighting of the second image relative to the first image?
A. Much stronger
B. About the same
C. Much weaker
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How is the lighting of the second image relative to the first image?
A. Much stronger
B. About the same
C. Much weaker
Answer with the option's letter from the given choices directly.

prompts: [["How is the lighting of the second image relative to the first image?\nA. Much stronger\nB. About the same\nC. Much weaker\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([-30.9375], 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]: 0.5537,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 708:  71%|▋| 709/999 [08:38<03:27
[Running Accuracy]: 0.5543,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 709:  71%|▋| 709/999 
 {'prompt': "A chat between a curious user and an artificial 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 lighting of the second image relative to the first image?\nA. Much stronger\nB. About the same\nC. Much weaker\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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([-31.3125], 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([-31.1875], 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]: 0.5543,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 709:  71%|▋| 710/999 
[Running Accuracy]: 0.5549,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 710:  71%|▋| 710/999 [08:39<03:22
 {'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>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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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([-30.8438], 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([-31.2969], 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]: 0.5549,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 710:  71%|▋| 711/999 [08:40<03:51
[Running Accuracy]: 0.5556,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 711:  71%|▋| 711/999 [08:40<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 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 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([-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([-31.2500], 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]: 0.5556,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 711:  71%|▋| 712/999 [08:41<03:3
[Running Accuracy]: 0.5562,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 712:  71%|▋| 712/999 [08:41<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 illumination sufficient in 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>Which part below is most affected by overexposure?
A. The sky in the second image
B. The dollar in the first image
C. The trees in the second image
D. The scissors 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 second image
B. The dollar in the first image
C. The trees in the second image
D. The scissors 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 second image\nB. The dollar in the first image\nC. The trees in the second image\nD. The scissors 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([-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]: 0.5562,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 712:  71%|▋| 713/999 [08:41<03:2
[Running Accuracy]: 0.5568,[Response]: A.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 713:  71
 {'prompt': "A chat between a curious user and an artificial 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 second image\nB. The dollar in the first image\nC. The trees in the second image\nD. The scissors 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 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([-31.3750], 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([-31.0781], 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]: 0.5568,[Response]: A.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 713:  71
[Running Accuracy]: 0.5574,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 714:  71%|▋| 714/999 [08:42<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>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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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([-31.3125], 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]: 0.5574,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 714:  72%|▋| 715/999 [08:43<03:0
[Running Accuracy]: 0.5580,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 715:  72%|▋| 715/999 [08:43<03:09
 {'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>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 is most severely affected by motion blur?
A. The ball in the second image
B. The figure's shadow in the first image
C. The background in the second image
D. The grass 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 ball in the second image
B. The figure's shadow in the first image
C. The background in the second image
D. The grass 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 ball in the second image\nB. The figure's shadow in the first image\nC. The background in the second image\nD. The grass in the first image\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([-31.4531], 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]: 0.5580,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 715:  72%|▋| 716/999 [08:43<03:26
[Running Accuracy]: 0.5587,[Response]: A.<|endoftext|>, [Correct Ans]: The ball in the second image, , [Prog]: 716:  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 motion blur?\nA. The ball in the second image\nB. The figure's shadow in the first image\nC. The background in the second image\nD. The grass 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 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([-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([-31.2969], 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]: 0.5587,[Response]: A.<|endoftext|>, [Correct Ans]: The ball in the second image, , [Prog]: 716:  7
[Running Accuracy]: 0.5593,[Response]: A.<|endoftext|>, [Correct Ans]: no, , [Prog]: 717:  72%|▋| 717/999 [08:44<03:21
 {'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 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 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 is 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 is 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([-31.5469], 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([-31.0156], 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]: 0.5593,[Response]: A.<|endoftext|>, [Correct Ans]: no, , [Prog]: 717:  72%|▋| 718/999 [08:45<03:15
[Running Accuracy]: 0.5585,[Response]: A.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 718:  72%|▋| 718/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. More authentic\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([-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([-30.8438], 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]: 0.5585,[Response]: A.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 718:  72%|▋| 719/999 
[Running Accuracy]: 0.5577,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 719:  72%|▋| 719/999 [08:45<03:07
 {'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 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 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([-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([-30.8750], 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]: 0.5577,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 719:  72%|▋| 720/999 [08:46<03:25
[Running Accuracy]: 0.5583,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 720:  72%|▋| 720/999 [08:46<03:25
 {'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>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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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. Ferris wheel in the second image
B. Characters in the first image
C. 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 is most affected by noise?
A. Ferris wheel in the second image
B. Characters in the first image
C. Sky in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most affected by noise?\nA. Ferris wheel in the second image\nB. Characters in the first image\nC. Sky 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([-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]: 0.5583,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 720:  72%|▋| 721/999 [08:47<03:18
[Running Accuracy]: 0.5576,[Response]: A.<|endoftext|>, [Correct Ans]: Characters in the first 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 is most affected by noise?\nA. Ferris wheel in the second image\nB. Characters in the first image\nC. 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. Similar
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 the second image?
A. Similar
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 the second image?\nA. Similar\nB. Much clearer\nC. Much blurrier\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([-31.4219], 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]: 0.5576,[Response]: A.<|endoftext|>, [Correct Ans]: Characters in the first image, , [Prog]: 721:  
[Running Accuracy]: 0.5568,[Response]: C.<|endoftext|>, [Correct Ans]: Similar, , [Prog]: 722:  72%|▋| 722/999 [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. Similar\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>Is the underexposure issue in the second image more severe than the first one?
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 underexposure issue in the second image more severe than the first one?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the underexposure issue in the second image more severe than the first one?\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([-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]: 0.5568,[Response]: C.<|endoftext|>, [Correct Ans]: Similar, , [Prog]: 722:  72%|▋| 723/999 [08:48<
[Running Accuracy]: 0.5574,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 723:  72%|▋| 723/999 [08:48<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 underexposure issue in the second image more severe than the first one?\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([-31.3750], 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([-31.3281], 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]: 0.5574,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 723:  72%|▋| 724/999 [08:49<02:5
[Running Accuracy]: 0.5580,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 724:  72%|▋| 724/999 [08:49<02:59
 {'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 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 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([-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([-30.8594], 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]: 0.5580,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 724:  73%|▋| 725/999 [08:49<02:59
[Running Accuracy]: 0.5572,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 725:  73%|▋| 725/999 [08:49<
 {'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 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>Is the noise issue of the first image more severe 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 noise issue of the first image more severe than the second image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the noise issue of the first image more severe 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([-31.2500], 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]: 0.5572,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 725:  73%|▋| 726/999 [08:50<
[Running Accuracy]: 0.5579,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 726:  73%|▋| 726/999 [08:50<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 noise issue of the first image more severe 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([-31.2188], 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([-31.1875], 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]: 0.5579,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 726:  73%|▋| 727/999 [08:51<02:5
[Running Accuracy]: 0.5571,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 727:  73%|▋| 727/999 [08:51<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 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 composition of the second 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 first image, how is the composition of the second image?
A. worse
B. 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 composition of the second 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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 B.

[Running Accuracy]: 0.5571,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 727:  73%|▋| 728/999 [08:51<02:5
[Running Accuracy]: 0.5577,[Response]: B.<|endoftext|>, [Correct Ans]: better, , [Prog]: 728:  73%|▋| 728/999 [08:51<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. 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 colors in these two images both very 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 very 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 very rich?\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([-31.2969], 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]: 0.5577,[Response]: B.<|endoftext|>, [Correct Ans]: better, , [Prog]: 728:  73%|▋| 729/999 [08:52<0
[Running Accuracy]: 0.5583,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 729:  73%|▋| 729/999 [08:52<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>Are the colors in these two images both very 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>How does the reality of the second image compare to the first image?
A. More real
B. About the same
C. Less real
Answer with the option's letter from the given choices directly.
 ASSISTANT:
using prompts
The first image: The second image:How does the reality of the second image compare to the first image?
A. More real
B. About the same
C. Less real
Answer with the option's letter from the given choices directly.

prompts: [["How does the reality of the second image compare to the first image?\nA. More real\nB. About the same\nC. Less real\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([-30.6094], 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]: 0.5583,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 729:  73%|▋| 730/999 [08:53<02:5
[Running Accuracy]: 0.5575,[Response]: C.<|endoftext|>, [Correct Ans]: More real, , [Prog]: 730:  73%|▋| 730/999 [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>How does the reality of the second image compare to the first image?\nA. More real\nB. About the same\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>Compared to the first image, how blurry 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:Compared to the first image, how blurry is 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 blurry 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([-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])
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]: 0.5575,[Response]: C.<|endoftext|>, [Correct Ans]: More real, , [Prog]: 730:  73%|▋| 731/999 [08:5
[Running Accuracy]: 0.5568,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 731:  73%|▋| 731/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 blurry 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>Which part below is most affected by noise?
A. The trees in the second image
B. The sky in the second image
C. The vehicles 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 trees in the second image
B. The sky in the second image
C. The vehicles 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 trees in the second image\nB. The sky in the second image\nC. The vehicles 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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.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]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 731:  73%|▋| 732/999 
[Running Accuracy]: 0.5560,[Response]: B.<|endoftext|>, [Correct Ans]: The vehicles in the first image, , [Prog]: 732:
 {'prompt': "A chat between a curious user and an artificial 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 trees in the second image\nB. The sky in the second image\nC. The vehicles 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 rich is the color 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 color 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 color 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([-31.1406], 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])
 B.

[Running Accuracy]: 0.5560,[Response]: B.<|endoftext|>, [Correct Ans]: The vehicles in the first image, , [Prog]: 732:
[Running Accuracy]: 0.5566,[Response]: B.<|endoftext|>, [Correct Ans]: richer, , [Prog]: 733:  73%|▋| 733/999 [08:55<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 rich is the color 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>Are the colors 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 colors of these two images both rich?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the colors of these two images both rich?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0938], 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([-30.3281], 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]: 0.5566,[Response]: B.<|endoftext|>, [Correct Ans]: richer, , [Prog]: 733:  73%|▋| 734/999 [08:56<0
[Running Accuracy]: 0.5572,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 734:  73%|▋| 734/999 [08:56<03:04
 {'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 the colors of these 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>Which part below is most severely affected by overexposure?
A. The faces of the people in the first image
B. The vehicles in the second image
C. The sky on the right side 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 faces of the people in the first image
B. The vehicles in the second image
C. The sky on the right side 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 faces of the people in the first image\nB. The vehicles in the second image\nC. The sky on the right side of 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([-31.2031], 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]: 0.5572,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 734:  74%|▋| 735/999 [08:57<03:26
[Running Accuracy]: 0.5578,[Response]: C.<|endoftext|>, [Correct Ans]: The sky on the right side 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 severely affected by overexposure?\nA. The faces of the people in the first image\nB. The vehicles in the second image\nC. The sky on the right side 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>Is the first image less 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 less 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 less realistic 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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.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]: The sky on the right side of the second image, 
[Running Accuracy]: 0.5571,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 736:  74%|▋| 736/999 [08:57<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 first image less 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 two images not sufficient?
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 two images not sufficient?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the illumination of the two images not sufficient?\nA. Yes\nB. No\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([-30.6094], 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])
 A.

[Running Accuracy]: 0.5571,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 736:  74%|▋| 737/999 [08:58<03:0
[Running Accuracy]: 0.5577,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 737:  74%|▋| 737/999 [08:58<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 illumination of the two images not sufficient?\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 clarity of the first 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 second image, how is the clarity of the first image?
A. About the same
B. Clearer
C. 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. About the same\nB. Clearer\nC. Blurrier\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([-31.0312], 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]: 0.5577,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 737:  74%|▋| 738/999 [08:59<03:0
[Running Accuracy]: 0.5583,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 738:  74%|▋| 738/999 [08:59<
 {'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>Compared to the second image, how is the clarity of the first image?\nA. About the same\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>Compared to the first image, how is the sharpness of the second image?
A. Slightly 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. Slightly 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. Slightly blurry\nB. Clearer\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([-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]: 0.5583,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 738:  74%|▋| 739/999 [09:00<
[Running Accuracy]: 0.5575,[Response]: A.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 739:  74%|▋| 739/999 [09:00<
 {'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 sharpness of the second image?\nA. Slightly 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>Are the colors of these two images both very 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 very 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 very rich?\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([-31.2344], 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]: 0.5575,[Response]: A.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 739:  74%|▋| 740/999 [09:00<
[Running Accuracy]: 0.5581,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 740:  74%|▋| 740/999 [09:00<03:17
 {'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 the colors of these two images both very 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 illumination of the first image less 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 less 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 less sufficient than the second image?\nA. No\nB. Yes\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([-31.3438], 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]: 0.5581,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 740:  74%|▋| 741/999 [09:01<03:27
[Running Accuracy]: 0.5587,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 741:  74%|▋| 741/999 [09:01<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 illumination of the first image less 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 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([-31.0312], 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([-30.6719], 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]: 0.5587,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 741:  74%|▋| 742/999 [09:02<03:1
[Running Accuracy]: 0.5580,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 742:  74%|▋| 742/999 [09:02<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 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 has the richest texture details?
A. Middle part of the second image
B. Bottom left ground of the first image
C. Pipe above 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. Middle part of the second image
B. Bottom left ground of the first image
C. Pipe above the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the richest texture details?\nA. Middle part of the second image\nB. Bottom left ground of the first image\nC. Pipe above 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([-30.6875], 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]: 0.5580,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 742:  74%|▋| 743/999 [09:02<03:0
[Running Accuracy]: 0.5572,[Response]: A.<|endoftext|>, [Correct Ans]: Pipe above the first image, , [Prog]: 743:  74%
 {'prompt': "A chat between a curious user and an artificial 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. Middle part of the second image\nB. Bottom left ground of the first image\nC. Pipe above 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 signboard in the second image
B. The bottleneck of 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 richest texture details?
A. The signboard in the second image
B. The bottleneck of 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 richest texture details?\nA. The signboard in the second image\nB. The bottleneck of the first image\nC. The ground in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0156], 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([-31.2500], 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]: 0.5572,[Response]: A.<|endoftext|>, [Correct Ans]: Pipe above the first image, , [Prog]: 743:  74%
[Running Accuracy]: 0.5578,[Response]: B.<|endoftext|>, [Correct Ans]: The bottleneck of the first image, , [Prog]: 74
 {'prompt': "A chat between a curious user and an artificial 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 signboard in the second image\nB. The bottleneck of the first image\nC. The 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 has the most serious underexposure?
A. Chinese characters on the wall in the second image
B. trees in the first image
C. left pillar 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 serious underexposure?
A. Chinese characters on the wall in the second image
B. trees in the first image
C. left pillar in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the most serious underexposure?\nA. Chinese characters on the wall in the second image\nB. trees in the first image\nC. left pillar in the second image\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([-31.0312], 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]: 0.5578,[Response]: B.<|endoftext|>, [Correct Ans]: The bottleneck of the first image, , [Prog]: 74
[Running Accuracy]: 0.5570,[Response]: B.<|endoftext|>, [Correct Ans]: left pillar in the second image, , [Prog]: 745:
 {'prompt': "A chat between a curious user and an artificial 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 serious underexposure?\nA. Chinese characters on the wall in the second image\nB. trees in the first image\nC. left pillar 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 sharpness of the first image higher 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 sharpness of the first image higher than 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 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([-31.1562], 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]: 0.5570,[Response]: B.<|endoftext|>, [Correct Ans]: left pillar in the second image, , [Prog]: 745:
[Running Accuracy]: 0.5563,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 746:  75%|▋| 746/999 [09:05<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 sharpness of the first image higher 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([-31.1562], 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]: 0.5563,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 746:  75%|▋| 747/999 [09:06<03:1
[Running Accuracy]: 0.5556,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 747:  75%|▋| 747/999 [09:06<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 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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. 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. worse
B. 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 composition of the second image?\nA. worse\nB. better\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([-31.3906], 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]: 0.5556,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 747:  75%|▋| 748/999 [09:06<03:0
[Running Accuracy]: 0.5561,[Response]: B.<|endoftext|>, [Correct Ans]: better, , [Prog]: 748:  75%|▋| 748/999 [09:06<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 composition of the second image?\nA. worse\nB. 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>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([-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])
 A.

[Running Accuracy]: 0.5561,[Response]: B.<|endoftext|>, [Correct Ans]: better, , [Prog]: 748:  75%|▋| 749/999 [09:07<0
[Running Accuracy]: 0.5567,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 749:  75%|▋| 749/999 [09:07<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 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>Compared to the first image, how rich is the color 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 rich is the color 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 rich is the color 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([-31.4531], 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([-31.0781], 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]: 0.5567,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 749:  75%|▊| 750/999 [09:08<02:5
[Running Accuracy]: 0.5560,[Response]: A.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 750:  75%|▊| 750/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. Similar\nB. Richer\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 rich is the color 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 rich is the color 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 rich is the color 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([-31.1094], 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([-31.5000], 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]: 0.5560,[Response]: A.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 750:  75%|▊| 751/999
[Running Accuracy]: 0.5566,[Response]: A.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 751:  75%|▊| 751/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. More monotonous\nB. More 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 suffers the most severe underexposure problem?
A. The right side of the first image
B. The people in the first image
C. The sky in the first image
D. The left side 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 suffers the most severe underexposure problem?
A. The right side of the first image
B. The people in the first image
C. The sky in the first image
D. The left side sky of the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below suffers the most severe underexposure problem?\nA. The right side of the first image\nB. The people in the first image\nC. The sky in the first image\nD. The left side sky of the second image\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([-31.3906], 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]: 0.5566,[Response]: A.<|endoftext|>, [Correct Ans]: More monotonous, , [Prog]: 751:  75%|▊| 752/999
[Running Accuracy]: 0.5572,[Response]: A.<|endoftext|>, [Correct Ans]: The right side of the first 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 suffers the most severe underexposure problem?\nA. The right side of the first image\nB. The people in the first image\nC. The sky in the first image\nD. The left side sky 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. The phone booth in the second image
B. The sky in the first image
C. The headlights 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 phone booth in the second image
B. The sky in the first image
C. The headlights 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 phone booth in the second image\nB. The sky in the first image\nC. The headlights in the second 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([-31.5469], 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]: 0.5572,[Response]: A.<|endoftext|>, [Correct Ans]: The right side of the first image, , [Prog]: 75
[Running Accuracy]: 0.5578,[Response]: C.<|endoftext|>, [Correct Ans]: The headlights in the second image, , [Prog]: 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 overexposure?\nA. The phone booth in the second image\nB. The sky in the first image\nC. The headlights 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 colors of these two images both very monotonous?
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 very monotonous?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the colors of these two images both very monotonous?\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([-31.1875], 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]: 0.5578,[Response]: C.<|endoftext|>, [Correct Ans]: The headlights in the second image, , [Prog]: 7
[Running Accuracy]: 0.5570,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 754:  75%|▊| 754/999 [09:11<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>Are the colors of these two images both very monotonous?\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 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([-30.2812], 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([-31.3125], 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]: 0.5570,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 754:  76%|▊| 755/999 [09:11<02:50
[Running Accuracy]: 0.5576,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 755:  76%|▊| 755/999 [09:11<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>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>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([-30.6875], 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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.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]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 755:  76%|▊| 756/999 [09:12<02:45
[Running Accuracy]: 0.5569,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 756:  76%|▊| 756/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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>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([-31.2031], 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([-30.1562], 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]: 0.5569,[Response]: B.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 756:  76%|▊| 757/999 
[Running Accuracy]: 0.5575,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 757:  76%|▊| 757/999 [09:13<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>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>Are the two images both of high authenticity?
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 high authenticity?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the two images both of high authenticity?\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([-31.1562], 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]: 0.5575,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 757:  76%|▊| 758/999 [09:13<02:4
[Running Accuracy]: 0.5580,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 758:  76%|▊| 758/999 [09:13<02:40
 {'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>Are the two images both of high authenticity?\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([-31.3750], 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([-30.5156], 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]: 0.5580,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 758:  76%|▊| 759/999 [09:14<02:37
[Running Accuracy]: 0.5573,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 759:  76%|▊| 759/999 [09:14<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 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 authenticity levels of these two images both very high?
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 authenticity levels of these two images both very high?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the authenticity levels of these two images both very high?\nA. No\nB. Yes\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([-31.3125], 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]: 0.5573,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 759:  76%|▊| 760/999 [09:14<02:3
[Running Accuracy]: 0.5579,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 760:  76%|▊| 760/999 [09:14<02:38
 {'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 authenticity levels of these two images both very high?\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 glowing text in the middle of the first image
B. The background of the first image
C. The flowers 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 glowing text in the middle of the first image
B. The background of the first image
C. The flowers 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 glowing text in the middle of the first image\nB. The background of the first image\nC. The flowers in 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([-31.1562], 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]: 0.5579,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 760:  76%|▊| 761/999 [09:15<02:39
[Running Accuracy]: 0.5585,[Response]: A.<|endoftext|>, [Correct Ans]: The glowing text in the middle of the first ima
 {'prompt': "A chat between a curious user and an artificial 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 glowing text in the middle of the first image\nB. The background of the first image\nC. The flowers 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 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 these two images both rich?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the colors 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])
 B.

[Running Accuracy]: 0.5585,[Response]: A.<|endoftext|>, [Correct Ans]: The glowing text in the middle of the first ima
[Running Accuracy]: 0.5591,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 762:  76%|▊| 762/999 [09:16<02:35
 {'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>Are the colors of these 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 of these 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 of these images not very realistic?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images not very realistic?\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([-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]: 0.5591,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 762:  76%|▊| 763/999 [09:17<02:38
[Running Accuracy]: 0.5583,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 763:  76%|▊| 763/999 [09:17<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>Are both of these images not 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 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([-31.5000], 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([-30.2031], 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]: 0.5583,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 763:  76%|▊| 764/999 [09:17<02:3
[Running Accuracy]: 0.5576,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 764:  76%|▊| 764/999 [09:17<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 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. 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([-30.9219], 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([-30.9844], 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]: 0.5576,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 764:  77%|▊| 765/999 [09:18<02:3
[Running Accuracy]: 0.5569,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 765:  77%|▊| 765/999 [09:18<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 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 not of high clarity?
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 of high clarity?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images not of high clarity?\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([-31.3125], 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]: 0.5569,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 765:  77%|▊| 766/999 [09:18<02:2
[Running Accuracy]: 0.5574,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 766:  77%|▊| 766/999 [09:18<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 not of high clarity?\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 underexposure?
A. The characters in the second image
B. The background wall in the first image
C. The lighting fixtures 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 underexposure?
A. The characters in the second image
B. The background wall in the first image
C. The lighting fixtures in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part below is most severely affected by underexposure?\nA. The characters in the second image\nB. The background wall in the first image\nC. The lighting fixtures 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([-31.1406], 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]: 0.5574,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 766:  77%|▊| 767/999 [09:19<02:4
[Running Accuracy]: 0.5567,[Response]: A.<|endoftext|>, [Correct Ans]: The background wall 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 underexposure?\nA. The characters in the second image\nB. The background wall in the first image\nC. The lighting fixtures 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([-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([-31.2344], 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]: 0.5567,[Response]: A.<|endoftext|>, [Correct Ans]: The background wall in the first image, , [Prog
[Running Accuracy]: 0.5560,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 768:  77%|▊| 768/999 [09:20<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>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 the authenticity of these two images both low?
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 low?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the authenticity of these two images both low?\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([-31.4062], 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]: 0.5560,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 768:  77%|▊| 769/999 [09:21<02:3
[Running Accuracy]: 0.5553,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 769:  77%|▊| 769/999 [09:21<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>Are the authenticity of these two images both low?\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 lighting 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 lighting issues in both of these images?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are there lighting issues in both of these 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([-30.4219], 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]: 0.5553,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 769:  77%|▊| 770/999 [09:21<02:3
[Running Accuracy]: 0.5545,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 770:  77%|▊| 770/999 [09:21<02:30
 {'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 there lighting 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>Are the colors of these two images not rich enough?
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 not rich enough?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the colors of these two images not rich enough?\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([-30.9062], 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]: 0.5545,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 770:  77%|▊| 771/999 [09:22<02:29
[Running Accuracy]: 0.5551,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 771:  77%|▊| 771/999 [09:22<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 the colors of these two images not rich enough?\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 authentic?
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 authentic?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images very authentic?\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([-31.2500], 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]: 0.5551,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 771:  77%|▊| 772/999 [09:22<02:3
[Running Accuracy]: 0.5557,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 772:  77%|▊| 772/999 [09:23<02:30
 {'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 of these images very authentic?\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([-31.4688], 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([-30.9531], 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]: 0.5557,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 772:  77%|▊| 773/999 [09:23<02:30
[Running Accuracy]: 0.5550,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 773:  77%|▊| 773/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 sharpness of the second image?
A. Higher
B. 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. Higher
B. 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. Higher\nB. Lower\nC. About the same\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-29.6250], 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([-31.1875], 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]: 0.5550,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 773:  77%|▊| 774/999 
[Running Accuracy]: 0.5543,[Response]: A.<|endoftext|>, [Correct Ans]: Lower, , [Prog]: 774:  77%|▊| 774/999 [09:24<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. Higher\nB. Lower\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 colors of these two images both very 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 very 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 very rich?\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([-31.5469], 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]: 0.5543,[Response]: A.<|endoftext|>, [Correct Ans]: Lower, , [Prog]: 774:  78%|▊| 775/999 [09:25<03
[Running Accuracy]: 0.5548,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 775:  78%|▊| 775/999 [09:25<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>Are the colors of these two images both very 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>Are the illumination of these two images both insufficient?
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 both insufficient?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the illumination of these two images both insufficient?\nA. No\nB. Yes\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([-31.1562], 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]: No, , [Prog]: 775:  78%|▊| 776/999 [09:26<03:13
[Running Accuracy]: 0.5554,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 776:  78%|▊| 776/999 [09:26<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>Are the illumination of these two images both insufficient?\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 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 vivid?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the colors of these two images vivid?\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([-31.3125], 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]: 0.5554,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 776:  78%|▊| 777/999 [09:27<02:5
[Running Accuracy]: 0.5560,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 777:  78%|▊| 777/999 [09:27<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>Are the colors of these two images vivid?\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 kind of distortion issues do not exist in these two images?
A. Out of focus
B. Halo trailing
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 kind of distortion issues do not exist in these two images?
A. Out of focus
B. Halo trailing
C. Noise
D. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion issues do not exist in these two images?\nA. Out of focus\nB. Halo trailing\nC. Noise\nD. Overexposure\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([-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]: 0.5560,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 777:  78%|▊| 778/999 [09:28<02:5
[Running Accuracy]: 0.5553,[Response]: B.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 778:  78%|▊| 778/999 [09:28<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 issues do not exist in these two images?\nA. Out of focus\nB. Halo trailing\nC. Noise\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>Which part is most affected by noise?
A. The characters in the first image
B. The characters in the second 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 is most affected by noise?
A. The characters in the first image
B. The characters in the second image
C. The ground in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most affected by noise?\nA. The characters in the first image\nB. The characters in the second image\nC. The ground in the second image\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([-31.0156], 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]: 0.5553,[Response]: B.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 778:  78%|▊| 779/999 [09:28<02
[Running Accuracy]: 0.5558,[Response]: A.<|endoftext|>, [Correct Ans]: The characters in the first image, , [Prog]: 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 is most affected by noise?\nA. The characters in the first image\nB. The characters in the second 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 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([-30.5000], 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])
 C.

[Running Accuracy]: 0.5558,[Response]: A.<|endoftext|>, [Correct Ans]: The characters in the first image, , [Prog]: 77
[Running Accuracy]: 0.5564,[Response]: C.<|endoftext|>, [Correct Ans]: Less vivid, , [Prog]: 780:  78%|▊| 780/999 [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 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 the colors of these two images both dim?
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 dim?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the colors of these two images both dim?\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([-30.4219], 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]: 0.5564,[Response]: C.<|endoftext|>, [Correct Ans]: Less vivid, , [Prog]: 780:  78%|▊| 781/999 [09:
[Running Accuracy]: 0.5557,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 781:  78%|▊| 781/999 [09:30<02:34
 {'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 dim?\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 texture detail 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 texture detail 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 texture detail 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([-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]: 0.5557,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 781:  78%|▊| 782/999 [09:30<02:30
[Running Accuracy]: 0.5563,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 782:  78%|▊| 782/999 [09:30<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. 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>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([-31.2500], 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([-30.3438], 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]: 0.5563,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 782:  78%|▊| 783/999 [09:31<0
[Running Accuracy]: 0.5568,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 783:  78%|▊| 783/999 [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>Are there any distortion issues in these two images?
A. Out of focus
B. Lens flare
C. 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. Out of focus
B. Lens flare
C. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["Are there any distortion issues in these two images?\nA. Out of focus\nB. Lens flare\nC. Overexposure\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([-31.4219], 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]: 0.5568,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 783:  78%|▊| 784/999 [09
[Running Accuracy]: 0.5561,[Response]: A.<|endoftext|>, [Correct Ans]: Lens flare, , [Prog]: 784:  78%|▊| 784/999 [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>Are there any distortion issues in these two images?\nA. Out of focus\nB. Lens flare\nC. 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>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([-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([-31.0938], 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]: 0.5561,[Response]: A.<|endoftext|>, [Correct Ans]: Lens flare, , [Prog]: 784:  79%|▊| 785/999 [09:
[Running Accuracy]: 0.5554,[Response]: C.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 785:  79%|▊| 785/999
 {'prompt': "A chat between a curious user and an artificial 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>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([-30.9844], 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])
 A.

[Running Accuracy]: 0.5554,[Response]: C.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 785:  79%|▊| 786/999
[Running Accuracy]: 0.5560,[Response]: A.<|endoftext|>, [Correct Ans]: no, , [Prog]: 786:  79%|▊| 786/999 [09:33<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>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>Which type of distortion issue exists in the second image but not in the first image?
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:Which type of distortion issue exists in the second image but not in the first image?
A. overexposure
B. noise
C. motion blur
Answer with the option's letter from the given choices directly.

prompts: [["Which type of distortion issue exists in the second image but not in the first image?\nA. overexposure\nB. noise\nC. motion blur\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([-30.6406], 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]: 0.5560,[Response]: A.<|endoftext|>, [Correct Ans]: no, , [Prog]: 786:  79%|▊| 787/999 [09:34<02:24
[Running Accuracy]: 0.5553,[Response]: A.<|endoftext|>, [Correct Ans]: noise, , [Prog]: 787:  79%|▊| 787/999 [09:34<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>Which type of distortion issue exists in the second image but not in the first image?\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>Is the texture detail of the first image much 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 much 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 much 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([-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([-31.1875], 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]: 0.5553,[Response]: A.<|endoftext|>, [Correct Ans]: noise, , [Prog]: 787:  79%|▊| 788/999 [09:35<02
[Running Accuracy]: 0.5546,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 788:  79%|▊| 788/999 [09:35<02:37
 {'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 texture detail of the first image much 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 part below is most severely affected by overexposure?
A. The audience in the first image
B. The hair 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 overexposure?
A. The audience in the first image
B. The hair 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 overexposure?\nA. The audience in the first image\nB. The hair 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.], device='cuda:0', dtype=torch.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]: 0.5546,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 788:  79%|▊| 789/999 [09:35<02:31
[Running Accuracy]: 0.5551,[Response]: C.<|endoftext|>, [Correct Ans]: The sky in the first 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. The audience in the first image\nB. The hair 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': '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, what is the texture detail like in 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:In comparison to the first image, what is the texture detail like in the second image?
A. Similar
B. Less rich
C. Richer
Answer with the option's letter from the given choices directly.

prompts: [["In comparison to the first image, what is the texture detail like in the second image?\nA. Similar\nB. Less rich\nC. Richer\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9688], 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([-30.8281], 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]: 0.5551,[Response]: C.<|endoftext|>, [Correct Ans]: The sky in the first image, , [Prog]: 789:  79%
[Running Accuracy]: 0.5544,[Response]: B.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 790:  79%|▊| 790/999 [09:36<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>In comparison to the first image, what is the texture detail like in 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>Which part below is most severely affected by noise?
A. Lawn in the first image
B. Wall in the second 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. Lawn in the first image
B. Wall in the second 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. Lawn in the first image\nB. Wall in the second image\nC. People in the second image\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([-31.4531], 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]: 0.5544,[Response]: B.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 790:  79%|▊| 791/999 [09:37<0
[Running Accuracy]: 0.5550,[Response]: A.<|endoftext|>, [Correct Ans]: Lawn in the first image, , [Prog]: 791:  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 noise?\nA. Lawn in the first image\nB. Wall in the second 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>Which part below is most severely affected by overexposure?
A. Reflection area in the first image
B. Train in the second image
C. Yellow bag 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. Reflection area in the first image
B. Train in the second image
C. Yellow bag 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. Reflection area in the first image\nB. Train in the second image\nC. Yellow bag in the second 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([-30.4062], 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]: 0.5550,[Response]: A.<|endoftext|>, [Correct Ans]: Lawn in the first image, , [Prog]: 791:  79%|▊|
[Running Accuracy]: 0.5556,[Response]: A.<|endoftext|>, [Correct Ans]: Reflection area in the first image, , [Prog]: 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 overexposure?\nA. Reflection area in the first image\nB. Train in the second image\nC. Yellow bag 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 most prominent graininess?
A. Facial part of the second image
B. Person in the first 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 most prominent graininess?
A. Facial part of the second image
B. Person in the first image
C. Sky in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the most prominent graininess?\nA. Facial part of the second image\nB. Person in the first image\nC. Sky in the 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([-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]: 0.5556,[Response]: A.<|endoftext|>, [Correct Ans]: Reflection area in the first image, , [Prog]: 7
[Running Accuracy]: 0.5549,[Response]: B.<|endoftext|>, [Correct Ans]: Facial part of the second image, , [Prog]: 793:
 {'prompt': "A chat between a curious user and an artificial 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 prominent graininess?\nA. Facial part of the second image\nB. Person in the first 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 sharpness of the second image?
A. About the same
B. Clearer
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 sharpness of the second image?
A. About the same
B. Clearer
C. More blurred
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. Clearer\nC. More blurred\nAnswer with the option's letter from the given choices directly.\n"]]
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])
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]: 0.5549,[Response]: B.<|endoftext|>, [Correct Ans]: Facial part of the second image, , [Prog]: 793:
[Running Accuracy]: 0.5542,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 794:  79%|▊| 794/999 [09:39<
 {'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 sharpness of the second image?\nA. About the same\nB. Clearer\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 has the most severe motion blur?
A. The ball in the second image
B. The tree in the first 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 has the most severe motion blur?
A. The ball in the second image
B. The tree in the first image
C. The sky in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the most severe motion blur?\nA. The ball in the second image\nB. The tree in the first image\nC. The sky in the first image\nAnswer with the option's letter from the given choices directly.\n"]]
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])
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]: 0.5542,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 794:  80%|▊| 795/999 [09:40<
[Running Accuracy]: 0.5547,[Response]: A.<|endoftext|>, [Correct Ans]: The ball in the second image, , [Prog]: 795:  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 has the most severe motion blur?\nA. The ball in the second image\nB. The tree in the first 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>Compared to the first image, how is the sharpness of the second image?
A. Similar
B. Sharper
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. Similar
B. Sharper
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. Similar\nB. Sharper\nC. Blurrier\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([-31.5312], 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]: 0.5547,[Response]: A.<|endoftext|>, [Correct Ans]: The ball in the second image, , [Prog]: 795:  8
[Running Accuracy]: 0.5553,[Response]: B.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 796:  80%|▊| 796/999 [09:40<
 {'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. Similar\nB. Sharper\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 motion blur?
A. The ground in the second image
B. The train in the second image
C. The cat 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 train in the second image
C. The cat 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 train in the second image\nC. The cat 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([-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]: 0.5553,[Response]: B.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 796:  80%|▊| 797/999 [09:41<
[Running Accuracy]: 0.5558,[Response]: B.<|endoftext|>, [Correct Ans]: The train in the second image, , [Prog]: 797:  
 {'prompt': "A chat between a curious user and an artificial 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 train in the second image\nC. The cat 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 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 overexposure?
A. The leaves 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 overexposure?\nA. The leaves 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([-31.4531], 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([-30.1406], 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]: 0.5558,[Response]: B.<|endoftext|>, [Correct Ans]: The train in the second image, , [Prog]: 797:  
[Running Accuracy]: 0.5551,[Response]: B.<|endoftext|>, [Correct Ans]: The sky in the first image, , [Prog]: 798:  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 leaves 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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. Higher
B. About the same
C. Lower
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. Higher
B. About the same
C. Lower
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. Higher\nB. About the same\nC. Lower\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([-30.9844], 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]: 0.5551,[Response]: B.<|endoftext|>, [Correct Ans]: The sky in the first image, , [Prog]: 798:  80%
[Running Accuracy]: 0.5544,[Response]: B.<|endoftext|>, [Correct Ans]: Higher, , [Prog]: 799:  80%|▊| 799/999 [09:42<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 sharpness of the second image?\nA. Higher\nB. About the same\nC. Lower\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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([-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([-30.7969], 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]: 0.5544,[Response]: B.<|endoftext|>, [Correct Ans]: Higher, , [Prog]: 799:  80%|▊| 800/999 [09:43<0
[Running Accuracy]: 0.5537,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 800:  80%|▊| 800/999 [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>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 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([-30.9688], 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([-31.0156], 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])
 C.

[Running Accuracy]: 0.5537,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 800:  80%|▊| 801/999 [09:44<
[Running Accuracy]: 0.5543,[Response]: C.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 801:  80%|▊| 801/999 [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 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>Which part below is most severely affected by noise?
A. The bone in the first image
B. The wall in the second image
C. The plate 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 bone in the first image
B. The wall in the second image
C. The plate 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 bone in the first image\nB. The wall in the second image\nC. The plate 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([-31.1406], 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]: 0.5543,[Response]: C.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 801:  80%|▊| 802/999 [09:4
[Running Accuracy]: 0.5536,[Response]: A.<|endoftext|>, [Correct Ans]: The wall in the second image, , [Prog]: 802:  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 bone in the first image\nB. The wall in the second image\nC. The plate 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 authenticity of these two images both relatively high?
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 authenticity of these two images both relatively high?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are the authenticity of these two images both relatively high?\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([-30.7656], 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]: 0.5536,[Response]: A.<|endoftext|>, [Correct Ans]: The wall in the second image, , [Prog]: 802:  8
[Running Accuracy]: 0.5542,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 803:  80%|▊| 803/999 [09:45<02:09
 {'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>Are the authenticity of these two images both relatively high?\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. 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([-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([-31.3281], 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]: 0.5542,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 803:  80%|▊| 804/999 [09:46<02:14
[Running Accuracy]: 0.5547,[Response]: B.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 804:  80%|▊| 804/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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>Are both of these images relatively high 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 both of these images relatively high in clarity?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images relatively high in clarity?\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([-31.1094], 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]: 0.5547,[Response]: B.<|endoftext|>, [Correct Ans]: Less authentic, , [Prog]: 804:  81%|▊| 805/999 
[Running Accuracy]: 0.5553,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 805:  81%|▊| 805/999 [09:46<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 both of these images relatively high in clarity?\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 has the most severe overexposure problem?
A. The trees in the second image
B. The ground 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 has the most severe overexposure problem?
A. The trees in the second image
B. The ground 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 has the most severe overexposure problem?\nA. The trees in the second image\nB. The ground 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([-31.0469], 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([-30.7969], 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]: 0.5553,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 805:  81%|▊| 806/999 [09:47<02:2
[Running Accuracy]: 0.5558,[Response]: C.<|endoftext|>, [Correct Ans]: The sky in the first image, , [Prog]: 806:  81%
 {'prompt': "A chat between a curious user and an artificial 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 problem?\nA. The trees in the second image\nB. The ground 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': '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. Less real
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 first image, how real is the second image?
A. Less real
B. More 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. Less real\nB. More real\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([-31.3125], 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]: 0.5558,[Response]: C.<|endoftext|>, [Correct Ans]: The sky in the first image, , [Prog]: 806:  81%
[Running Accuracy]: 0.5551,[Response]: C.<|endoftext|>, [Correct Ans]: Less real, , [Prog]: 807:  81%|▊| 807/999 [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 real is the second image?\nA. Less real\nB. More real\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 color vividness 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 color vividness 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 color vividness 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([-31.1406], 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([-31.2969], 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]: 0.5551,[Response]: C.<|endoftext|>, [Correct Ans]: Less real, , [Prog]: 807:  81%|▊| 808/999 [09:4
[Running Accuracy]: 0.5557,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 808:  81%|▊| 808/999 [09:48<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 color vividness 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 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([-31.2500], 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])
 B.

[Running Accuracy]: 0.5557,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 808:  81%|▊| 809/999 [09:49<02:1
[Running Accuracy]: 0.5562,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 809:  81%|▊| 809/999 [09:49<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 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 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([-30.9062], 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])
 B.

[Running Accuracy]: 0.5562,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 809:  81%|▊| 810/999 [09:50<02:1
[Running Accuracy]: 0.5556,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 810:  81%|▊| 810/999 [09:50<02:10
 {'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 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 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([-30.5156], 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([-30.8750], 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]: 0.5556,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 810:  81%|▊| 811/999 [09:50<02:08
[Running Accuracy]: 0.5549,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 811:  81%|▊| 811/999 [09:50<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 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>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([-31.6094], 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])
 A.

[Running Accuracy]: 0.5549,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 811:  81%|▊| 812/999 [09:51<02:0
[Running Accuracy]: 0.5554,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 812:  81%|▊| 812/999 [09:51<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 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 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([-31.1094], 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([-31.3594], 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]: 0.5554,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 812:  81%|▊| 813/999 [09:52<02:0
[Running Accuracy]: 0.5560,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 813:  81%|▊| 813/999 [09:52<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 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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([-31.1250], 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([-31.1094], 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]: 0.5560,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 813:  81%|▊| 814/999 [09:52<02:0
[Running Accuracy]: 0.5565,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 814:  81%|▊| 814/999 [09:52<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 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>Which part below is most severely affected by overexposure?
A. Lotus leaf in the first image
B. Upper right light source in the second image
C. Fish 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. Lotus leaf in the first image
B. Upper right light source in the second image
C. Fish 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. Lotus leaf in the first image\nB. Upper right light source in the second image\nC. Fish in the first image\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([-31.2500], 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]: 0.5565,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 814:  82%|▊| 815/999 [09:53<02:0
[Running Accuracy]: 0.5571,[Response]: B.<|endoftext|>, [Correct Ans]: Upper right 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. Lotus leaf in the first image\nB. Upper right light source in the second image\nC. Fish 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 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([-31.1094], 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([-31.5312], 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]: 0.5571,[Response]: B.<|endoftext|>, [Correct Ans]: Upper right light source in the second image, ,
[Running Accuracy]: 0.5576,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 816:  82%|▊| 816/999 [09:54<02:02
 {'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>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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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. Left side of the vehicle in the first image
B. Person 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 is most affected by motion blur?
A. Left side of the vehicle in the first image
B. Person in the second image
C. Ground 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. Left side of the vehicle in the first image\nB. Person in the second image\nC. Ground in the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.9375], 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([-31.1094], 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]: 0.5576,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 816:  82%|▊| 817/999 [09:54<02:01
[Running Accuracy]: 0.5569,[Response]: B.<|endoftext|>, [Correct Ans]: Left side of the vehicle 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 is most affected by motion blur?\nA. Left side of the vehicle in the first image\nB. Person 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>Compared to the first image, how is the sharpness of the second image?
A. Similar
B. Sharper
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. Sharper
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. Sharper\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([-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])
 C.

[Running Accuracy]: 0.5569,[Response]: B.<|endoftext|>, [Correct Ans]: Left side of the vehicle in the first image, , 
[Running Accuracy]: 0.5562,[Response]: C.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 818:  82%|▊| 818/999 [09:55<
 {'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 sharpness of the second image?\nA. Similar\nB. Sharper\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>Which part is most severely affected by defocusing?
A. Background of the first image
B. Paper of the first image
C. Red object 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 severely affected by defocusing?
A. Background of the first image
B. Paper of the first image
C. Red object of the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most severely affected by defocusing?\nA. Background of the first image\nB. Paper of the first image\nC. Red object of 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([-31.0938], 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]: 0.5562,[Response]: C.<|endoftext|>, [Correct Ans]: Sharper, , [Prog]: 818:  82%|▊| 819/999 [09:56<
[Running Accuracy]: 0.5556,[Response]: B.<|endoftext|>, [Correct Ans]: Red object of the second image, , [Prog]: 819: 
 {'prompt': "A chat between a curious user and an artificial 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 defocusing?\nA. Background of the first image\nB. Paper of the first image\nC. Red object 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>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([-31.0156], 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([-30.9844], 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]: 0.5556,[Response]: B.<|endoftext|>, [Correct Ans]: Red object of the second image, , [Prog]: 819: 
[Running Accuracy]: 0.5561,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 820:  82%|▊| 820/999 [09:57<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>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>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([-31.4375], 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]: 0.5561,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 820:  82%|▊| 821/999 [09:57<02:0
[Running Accuracy]: 0.5554,[Response]: C.<|endoftext|>, [Correct Ans]: Less vivid, , [Prog]: 821:  82%|▊| 821/999 [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 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>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([-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]: 0.5554,[Response]: C.<|endoftext|>, [Correct Ans]: Less vivid, , [Prog]: 821:  82%|▊| 822/999 [09:
[Running Accuracy]: 0.5547,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 822:  82%|▊| 822/999 [09:58<
 {'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>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': '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. 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.], device='cuda:0', dtype=torch.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]: 0.5547,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 822:  82%|▊| 823/999 [09:59<
[Running Accuracy]: 0.5541,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 823:  82%|▊| 823/999 [09:59<
 {'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>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': '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 magazine in the second image
B. The train in the first image
C. The bookshelf 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 magazine in the second image
B. The train in the first image
C. The bookshelf 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 magazine in the second image\nB. The train in the first image\nC. The bookshelf 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([-31.3594], 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]: 0.5541,[Response]: C.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 823:  82%|▊| 824/999 [09:59<
[Running Accuracy]: 0.5546,[Response]: B.<|endoftext|>, [Correct Ans]: The train in the first image, , [Prog]: 824:  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 motion blur?\nA. The magazine in the second image\nB. The train in the first image\nC. The bookshelf 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 is the lighting of the second image compared to the first 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:How is the lighting of the second image compared to the first image?
A. more sufficient
B. less sufficient
C. about the same
Answer with the option's letter from the given choices directly.

prompts: [["How is the lighting of the second image compared to the first 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([-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([-30.9688], 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]: 0.5546,[Response]: B.<|endoftext|>, [Correct Ans]: The train in the first image, , [Prog]: 824:  8
[Running Accuracy]: 0.5552,[Response]: B.<|endoftext|>, [Correct Ans]: less sufficient, , [Prog]: 825:  83%|▊| 825/999
 {'prompt': "A chat between a curious user and an artificial 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 lighting of the second image compared to the first 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>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([-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]: 0.5552,[Response]: B.<|endoftext|>, [Correct Ans]: less sufficient, , [Prog]: 825:  83%|▊| 826/999
[Running Accuracy]: 0.5545,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 826:  83%|▊| 826/999 [10:01<01:53
 {'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 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 below is most severely affected by noise?
A. The sky in the second image
B. The background in the first image
C. The 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 is most severely affected by noise?
A. The sky in the second image
B. The background in the first image
C. The facial features of 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 noise?\nA. The sky in the second image\nB. The background in the first image\nC. The facial features of the person 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([-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]: 0.5545,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 826:  83%|▊| 827/999 [10:01<01:54
[Running Accuracy]: 0.5538,[Response]: B.<|endoftext|>, [Correct Ans]: The sky 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 below is most severely affected by noise?\nA. The sky in the second image\nB. The background in the first image\nC. The 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>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([-31.2500], 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([-30.8438], 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]: 0.5538,[Response]: B.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 827:  83
[Running Accuracy]: 0.5531,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 828:  83%|▊| 828/999 [10:02<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>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 noise?
A. The sky in the second image
B. The ground 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 below is most affected by noise?
A. The sky in the second image
B. The ground 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 below is most affected by noise?\nA. The sky in the second image\nB. The ground 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([-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])
 B.

[Running Accuracy]: 0.5531,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 828:  83%|▊| 829/999 [10:03<01:49
[Running Accuracy]: 0.5525,[Response]: B.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 829:  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 below is most affected by noise?\nA. The sky in the second image\nB. The ground 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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. Lotus in the second image
B. Horse in the first image
C. Glass 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. Lotus in the second image
B. Horse in the first image
C. Glass 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. Lotus in the second image\nB. Horse in the first image\nC. Glass 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([-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]: 0.5525,[Response]: B.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 829:  83
[Running Accuracy]: 0.5530,[Response]: B.<|endoftext|>, [Correct Ans]: Horse in the first image, , [Prog]: 830:  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 below is most severely affected by motion blur?\nA. Lotus in the second image\nB. Horse in the first image\nC. Glass 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 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([-31.2188], 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([-30.8594], 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]: 0.5530,[Response]: B.<|endoftext|>, [Correct Ans]: Horse in the first image, , [Prog]: 830:  83%|▊
[Running Accuracy]: 0.5535,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 831:  83%|▊| 831/999 [10:04<
 {'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. 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 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 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 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([-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([-30.6719], 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]: 0.5535,[Response]: B.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 831:  83%|▊| 832/999 [10:05<
[Running Accuracy]: 0.5541,[Response]: A.<|endoftext|>, [Correct Ans]: no, , [Prog]: 832:  83%|▊| 832/999 [10:05<01:59
 {'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 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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. Bridge in the second image
C. Surface of the water 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 first image
B. Bridge in the second image
C. Surface of the water 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 first image\nB. Bridge in the second image\nC. Surface of the water 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([-31.4219], 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]: 0.5541,[Response]: A.<|endoftext|>, [Correct Ans]: no, , [Prog]: 832:  83%|▊| 833/999 [10:06<02:08
[Running Accuracy]: 0.5546,[Response]: A.<|endoftext|>, [Correct Ans]: Background of the first image, , [Prog]: 833:  
 {'prompt': "A chat between a curious user and an artificial 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. Bridge in the second image\nC. Surface of the water 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 people in the second image
B. The vehicle in the first image
C. The 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 is most affected by motion blur?
A. The people in the second image
B. The vehicle in the first image
C. The leaves 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 people in the second image\nB. The vehicle in the first image\nC. The leaves 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([-29.3281], 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]: 0.5546,[Response]: A.<|endoftext|>, [Correct Ans]: Background of the first image, , [Prog]: 833:  
[Running Accuracy]: 0.5540,[Response]: A.<|endoftext|>, [Correct Ans]: The leaves in the first image, , [Prog]: 834:  
 {'prompt': "A chat between a curious user and an artificial 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 people in the second image\nB. The vehicle in the first image\nC. The 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>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([-30.8281], 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([-30.5938], 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]: 0.5540,[Response]: A.<|endoftext|>, [Correct Ans]: The leaves in the first image, , [Prog]: 834:  
[Running Accuracy]: 0.5533,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 835:  84%|▊| 835/999 [10:07<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. 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. 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 clarity 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 clarity 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([-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])
 C.

[Running Accuracy]: 0.5533,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 835:  84%|▊| 836/999 [10:08<01:5
[Running Accuracy]: 0.5538,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 836:  84%|▊| 836/999 [10:08
 {'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 clarity 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 is most affected by the halo drag of the image?
A. The flowers in the first image
B. The tabletop in the first image
C. The taillights of the black 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 is most affected by the halo drag of the image?
A. The flowers in the first image
B. The tabletop in the first image
C. The taillights of the black vehicle in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most affected by the halo drag of the image?\nA. The flowers in the first image\nB. The tabletop in the first image\nC. The taillights of the black vehicle 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([-31.0781], 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]: 0.5538,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 836:  84%|▊| 837/999 [10:09
[Running Accuracy]: 0.5544,[Response]: C.<|endoftext|>, [Correct Ans]: The taillights of the black vehicle in the seco
 {'prompt': "A chat between a curious user and an artificial 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 halo drag of the image?\nA. The flowers in the first image\nB. The tabletop in the first image\nC. The taillights of the black vehicle 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 brightness of the second image?
A. Similar
B. Brighter
C. Duller
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 brightness of the second image?
A. Similar
B. Brighter
C. Duller
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the brightness of the second image?\nA. Similar\nB. Brighter\nC. Duller\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([-30.5625], 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])
 B.

[Running Accuracy]: 0.5544,[Response]: C.<|endoftext|>, [Correct Ans]: The taillights of the black vehicle in the seco
[Running Accuracy]: 0.5549,[Response]: B.<|endoftext|>, [Correct Ans]: Brighter, , [Prog]: 838:  84%|▊| 838/999 [10:09
 {'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 brightness of the second image?\nA. Similar\nB. Brighter\nC. Duller\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 cat in the second image
B. The object in the center of 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 is most severely affected by noise?
A. The cat in the second image
B. The object in the center of the first image
C. The background of the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most severely affected by noise?\nA. The cat in the second image\nB. The object in the center of the first image\nC. The background of the first 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([-31.0938], 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]: 0.5549,[Response]: B.<|endoftext|>, [Correct Ans]: Brighter, , [Prog]: 838:  84%|▊| 839/999 [10:10
[Running Accuracy]: 0.5542,[Response]: B.<|endoftext|>, [Correct Ans]: The cat in the second image, , [Prog]: 839:  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 noise?\nA. The cat in the second image\nB. The object in the center of the first 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>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([-30.9844], 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([-31.4219], 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]: 0.5542,[Response]: B.<|endoftext|>, [Correct Ans]: The cat in the second image, , [Prog]: 839:  84
[Running Accuracy]: 0.5536,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 840:  84%|▊| 840/999 [10:10<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 blurrier 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 most vibrant colors?
A. The sky in the second image
B. The background of the first image
C. The upper shadow 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 vibrant colors?
A. The sky in the second image
B. The background of the first image
C. The upper shadow of the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the most vibrant colors?\nA. The sky in the second image\nB. The background of the first image\nC. The upper shadow 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([-31.2188], 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]: 0.5536,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 840:  84%|▊| 841/999 [10:11<01:4
[Running Accuracy]: 0.5541,[Response]: B.<|endoftext|>, [Correct Ans]: The background of the first image, , [Prog]: 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 has the most vibrant colors?\nA. The sky in the second image\nB. The background of the first image\nC. The upper shadow 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 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([-31.2500], 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([-30.9531], 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]: 0.5541,[Response]: B.<|endoftext|>, [Correct Ans]: The background of the first image, , [Prog]: 84
[Running Accuracy]: 0.5534,[Response]: A.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 842:  84%|▊| 842/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 lowest realism?
A. Characters in the second image
B. Floating objects in the first image
C. Wine glass 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 lowest realism?
A. Characters in the second image
B. Floating objects in the first image
C. Wine glass in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the lowest realism?\nA. Characters in the second image\nB. Floating objects in the first image\nC. Wine glass 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([-30.5156], 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]: 0.5534,[Response]: A.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 842:  84%|▊| 843/999 
[Running Accuracy]: 0.5540,[Response]: B.<|endoftext|>, [Correct Ans]: Floating objects 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 has the lowest realism?\nA. Characters in the second image\nB. Floating objects in the first image\nC. Wine glass 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 the most blurred?
A. The pink stones in the first image
B. The red pattern in the second image
C. The beach 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 the most blurred?
A. The pink stones in the first image
B. The red pattern in the second image
C. The beach in the first image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is the most blurred?\nA. The pink stones in the first image\nB. The red pattern in the second image\nC. The beach 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([-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]: 0.5540,[Response]: B.<|endoftext|>, [Correct Ans]: Floating objects in the first image, , [Prog]: 
[Running Accuracy]: 0.5533,[Response]: C.<|endoftext|>, [Correct Ans]: The red pattern 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 the most blurred?\nA. The pink stones in the first image\nB. The red pattern in the second image\nC. The beach 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 the first image much 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 much 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 much clearer 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([-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]: 0.5533,[Response]: C.<|endoftext|>, [Correct Ans]: The red pattern in the second image, , [Prog]: 
[Running Accuracy]: 0.5527,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 845:  85%|▊| 845/999 [10:14<01:42
 {'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 much 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 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([-31.5000], 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([-30.8906], 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]: 0.5527,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 845:  85%|▊| 846/999 [10:14<01:43
[Running Accuracy]: 0.5520,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 846:  85%|▊| 846/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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>Which part below is most severely affected by overexposure?
A. Figures in the first image
B. Sky in the second 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 below is most severely affected by overexposure?
A. Figures in the first image
B. Sky in the second image
C. Figures 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. Figures in the first image\nB. Sky in the second image\nC. Figures in the second 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([-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]: 0.5520,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 846:  85%|▊| 847/999 
[Running Accuracy]: 0.5525,[Response]: B.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 847:  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 overexposure?\nA. Figures in the first image\nB. Sky in the second image\nC. Figures 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 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([-30.0312], 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([-31.1875], 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]: 0.5525,[Response]: B.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 847:  85%|▊|
[Running Accuracy]: 0.5531,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 848:  85%|▊| 848/999 [10:16<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. 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. 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([-30.9062], 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]: 0.5531,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 848:  85%|▊| 849/999 [10:16<01:3
[Running Accuracy]: 0.5524,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 849:  85%|▊| 849/999 [10:16<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 blurrier 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 background tiles in the first image
B. The characters in the second image
C. The adult's hand 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 background tiles in the first image
B. The characters in the second image
C. The adult's hand 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 background tiles in the first image\nB. The characters in the second image\nC. The adult's hand 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([-30.3594], 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]: 0.5524,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 849:  85%|▊| 850/999 [10:17<01:3
[Running Accuracy]: 0.5518,[Response]: B.<|endoftext|>, [Correct Ans]: The adult's hand 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 is most severely affected by motion blur?\nA. The background tiles in the first image\nB. The characters in the second image\nC. The adult's hand 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 is most affected by the glare in the following images?
A. The red signboard in the second image
B. The computer 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 affected by the glare in the following images?
A. The red signboard in the second image
B. The computer 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 affected by the glare in the following images?\nA. The red signboard in the second image\nB. The computer 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([-31.3906], 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([-30.9688], 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]: 0.5518,[Response]: B.<|endoftext|>, [Correct Ans]: The adult's hand in the first image, , [Prog]: 
[Running Accuracy]: 0.5523,[Response]: A.<|endoftext|>, [Correct Ans]: The red signboard 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 glare in the following images?\nA. The red signboard in the second image\nB. The computer 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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. Blueberries in the first image
B. Crowd in the second image
C. 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. Blueberries in the first image
B. Crowd in the second image
C. 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. Blueberries in the first image\nB. Crowd in the second image\nC. Light source 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([-30.9531], 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]: 0.5523,[Response]: A.<|endoftext|>, [Correct Ans]: The red signboard in the second image, , [Prog]
[Running Accuracy]: 0.5528,[Response]: C.<|endoftext|>, [Correct Ans]: Light source in the second image, , [Prog]: 852
 {'prompt': "A chat between a curious user and an artificial 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. Blueberries in the first image\nB. Crowd in the second image\nC. 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>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([-31.4219], 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([-31.1250], 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]: 0.5528,[Response]: C.<|endoftext|>, [Correct Ans]: Light source in the second image, , [Prog]: 852
[Running Accuracy]: 0.5522,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 853:  85%|▊| 853/999 [10:19<01:33
 {'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 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 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([-31.2031], 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([-31.2812], 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]: 0.5522,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 853:  85%|▊| 854/999 [10:20<01:31
[Running Accuracy]: 0.5527,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 854:  85%|▊| 854/999 [10:20<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>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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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. Characters in the second image
B. Clip in the first image
C. 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 below is most severely affected by motion blur?
A. Characters in the second image
B. Clip in the first image
C. Background 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. Characters in the second image\nB. Clip in the first image\nC. Background in the first 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([-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]: 0.5527,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 854:  86%|▊| 855/999 [10:20<01:3
[Running Accuracy]: 0.5532,[Response]: A.<|endoftext|>, [Correct Ans]: Characters in the second image, , [Prog]: 855: 
 {'prompt': "A chat between a curious user and an artificial 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. Characters in the second image\nB. Clip in the first image\nC. 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>How is the lighting in the second image compared 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 lighting in the second image compared 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 lighting in the second image compared 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([-31.2500], 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([-31.3125], 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]: 0.5532,[Response]: A.<|endoftext|>, [Correct Ans]: Characters in the second image, , [Prog]: 855: 
[Running Accuracy]: 0.5526,[Response]: C.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 856:  86%|▊| 856/999
 {'prompt': "A chat between a curious user and an artificial 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 lighting in the second image compared 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>Which part is most severely affected by defocusing?
A. Dandelion in the first image
B. Background in the first image
C. Billboard 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 defocusing?
A. Dandelion in the first image
B. Background in the first image
C. Billboard in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most severely affected by defocusing?\nA. Dandelion in the first image\nB. Background in the first image\nC. Billboard 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([-30.7812], 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]: 0.5526,[Response]: C.<|endoftext|>, [Correct Ans]: More sufficient, , [Prog]: 856:  86%|▊| 857/999
[Running Accuracy]: 0.5519,[Response]: C.<|endoftext|>, [Correct Ans]: Background in the first 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 is most severely affected by defocusing?\nA. Dandelion in the first image\nB. Background in the first image\nC. Billboard 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 motion blur?
A. The sky in the first image
B. The motorcycle in the first image
C. The person 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. The sky in the first image
B. The motorcycle in the first image
C. The person 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. The sky in the first image\nB. The motorcycle in the first image\nC. The person on the left side 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([-31.2031], 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]: 0.5519,[Response]: C.<|endoftext|>, [Correct Ans]: Background in the first image, , [Prog]: 857:  
[Running Accuracy]: 0.5513,[Response]: B.<|endoftext|>, [Correct Ans]: The person 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 below is most severely affected by motion blur?\nA. The sky in the first image\nB. The motorcycle in the first image\nC. The person 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>Which part is most severely affected by motion blur?
A. Flowers in the bottom left corner of the second image
B. Fish in the first image
C. Leaves in the bottom right corner 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 severely affected by motion blur?
A. Flowers in the bottom left corner of the second image
B. Fish in the first image
C. Leaves in the bottom right corner of 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. Flowers in the bottom left corner of the second image\nB. Fish in the first image\nC. Leaves in the bottom right corner of 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([-31.3125], 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]: 0.5513,[Response]: B.<|endoftext|>, [Correct Ans]: The person on the left side in the second image
[Running Accuracy]: 0.5518,[Response]: A.<|endoftext|>, [Correct Ans]: Flowers in the bottom left 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 is most severely affected by motion blur?\nA. Flowers in the bottom left corner of the second image\nB. Fish in the first image\nC. Leaves in the bottom right corner 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. The earphones in the first image
B. The ground 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 earphones in the first image
B. The ground 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 earphones in the first image\nB. The ground 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([-31.2344], 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([-30.9688], 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]: 0.5518,[Response]: A.<|endoftext|>, [Correct Ans]: Flowers in the bottom left corner of the second
[Running Accuracy]: 0.5523,[Response]: C.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 860:  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 overexposure?\nA. The earphones in the first image\nB. The ground 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>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([-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([-31.8750], 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]: 0.5523,[Response]: C.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 860:  86
[Running Accuracy]: 0.5528,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 861:  86%|▊| 861/999 [10:25<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 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>Which part below has the most severe overexposure?
A. The ground in the first image
B. The ground in the second image
C. The motorcycle 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 ground in the first image
B. The ground in the second image
C. The motorcycle 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 ground in the first image\nB. The ground in the second image\nC. The motorcycle in the first 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([-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]: 0.5528,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 861:  86%|▊| 862/999 [10:25<01:3
[Running Accuracy]: 0.5522,[Response]: A.<|endoftext|>, [Correct Ans]: The ground in the second image, , [Prog]: 862: 
 {'prompt': "A chat between a curious user and an artificial 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 ground in the first image\nB. The ground in the second image\nC. The motorcycle 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. 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([-31.4062], 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([-31.1406], 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]: 0.5522,[Response]: A.<|endoftext|>, [Correct Ans]: The ground in the second image, , [Prog]: 862: 
[Running Accuracy]: 0.5527,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 863:  86%|▊| 863/999 [10:26<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 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 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([-31.4219], 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([-31.3594], 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]: 0.5527,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 863:  86%|▊| 864/999 [10:27<01:3
[Running Accuracy]: 0.5532,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 864:  86%|▊| 864/999 [10:27<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. 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. 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([-31.4062], 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([-31.5625], 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])
 B.

[Running Accuracy]: 0.5532,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 864:  87%|▊| 865/999 [10:27<01:3
[Running Accuracy]: 0.5526,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 865:  87%|▊| 865/999 [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. More blurry\nB. About the same\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 illumination 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 illumination 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 illumination 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([-31.4062], 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([-30.3594], 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]: 0.5526,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 865:  87%|▊| 866/999 [10
[Running Accuracy]: 0.5531,[Response]: A.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 866:  87%|▊| 866/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 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 the snowflake-like distortion?
A. The sky in the first image
B. The people 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 the snowflake-like distortion?
A. The sky in the first image
B. The people 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 the snowflake-like distortion?\nA. The sky in the first image\nB. The people 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([-30.8906], 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])
 C.

[Running Accuracy]: 0.5531,[Response]: A.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 866:  87%|▊| 867/999
[Running Accuracy]: 0.5536,[Response]: C.<|endoftext|>, [Correct Ans]: The ground in the second image, , [Prog]: 867: 
 {'prompt': "A chat between a curious user and an artificial 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 the snowflake-like distortion?\nA. The sky in the first image\nB. The people 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': '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. 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 would you rate the realism of 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 would you rate the realism of 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([-30.7969], 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([-31.0938], 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]: 0.5536,[Response]: C.<|endoftext|>, [Correct Ans]: The ground in the second image, , [Prog]: 867: 
[Running Accuracy]: 0.5541,[Response]: C.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 868:  87%|▊| 868/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. 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 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([-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]: 0.5541,[Response]: C.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 868:  87%|▊| 869/999 
[Running Accuracy]: 0.5547,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 869:  87%|▊| 869/999 [10:30<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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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([-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([-31.2500], 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]: 0.5547,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 869:  87%|▊| 870/999 [10:31<01:2
[Running Accuracy]: 0.5552,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 870:  87%|▊| 870/999 [10:31
 {'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. 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>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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.Size([1, 729, 1152])
vlm_emd torch.Size([1, 729, 1152])
all_hidden_state shape: torch.Size([2, 729, 1152])
 A.

[Running Accuracy]: 0.5552,[Response]: B.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 870:  87%|▊| 871/999 [10:31
[Running Accuracy]: 0.5557,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 871:  87%|▊| 871/999 [10:31<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 most affected by motion blur?
A. The animal in the top right corner of the second image
B. The motorcycle in the first image
C. The seaweed at the bottom 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 motion blur?
A. The animal in the top right corner of the second image
B. The motorcycle in the first image
C. The seaweed at the bottom of 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 animal in the top right corner of the second image\nB. The motorcycle in the first image\nC. The seaweed at the bottom of 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([-30.9219], 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]: 0.5557,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 871:  87%|▊| 872/999 [10:32<01:2
[Running Accuracy]: 0.5562,[Response]: A.<|endoftext|>, [Correct Ans]: The animal in the top right corner of the secon
 {'prompt': "A chat between a curious user and an artificial 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 top right corner of the second image\nB. The motorcycle in the first image\nC. The seaweed at the bottom 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>Compared to the first image, how 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, how 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, how 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([-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([-30.8906], 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]: 0.5562,[Response]: A.<|endoftext|>, [Correct Ans]: The animal in the top right corner of the secon
[Running Accuracy]: 0.5567,[Response]: B.<|endoftext|>, [Correct Ans]: richer, , [Prog]: 873:  87%|▊| 873/999 [10:33<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, how 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>Which part is most severely affected by defocusing?
A. The people in the first image
B. The background of the first image
C. The wooden pavilion 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 defocusing?
A. The people in the first image
B. The background of the first image
C. The wooden pavilion in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most severely affected by defocusing?\nA. The people in the first image\nB. The background of the first image\nC. The wooden pavilion 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([-31.3594], 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]: 0.5567,[Response]: B.<|endoftext|>, [Correct Ans]: richer, , [Prog]: 873:  87%|▊| 874/999 [10:33<0
[Running Accuracy]: 0.5561,[Response]: A.<|endoftext|>, [Correct Ans]: The wooden pavilion 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 is most severely affected by defocusing?\nA. The people in the first image\nB. The background of the first image\nC. The wooden pavilion 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 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([-30.3281], 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([-31.1094], 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]: 0.5561,[Response]: A.<|endoftext|>, [Correct Ans]: The wooden pavilion in the second image, , [Pro
[Running Accuracy]: 0.5554,[Response]: B.<|endoftext|>, [Correct Ans]: Worse, , [Prog]: 875:  88%|▉| 875/999 [10:34<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. Similar\nB. Better\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 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([-31.0156], 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([-30.8906], 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]: 0.5554,[Response]: B.<|endoftext|>, [Correct Ans]: Worse, , [Prog]: 875:  88%|▉| 876/999 [10:35<01
[Running Accuracy]: 0.5548,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 876:  88%|▉| 876/999 [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. 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 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([-30.9062], 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])
 C.

[Running Accuracy]: 0.5548,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 876:  88%|▉| 877/999 [10
[Running Accuracy]: 0.5542,[Response]: C.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 877:  88%|▉| 877/999 [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. 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 color richness of 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 color richness of 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 color richness of 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([-31.5000], 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]: 0.5542,[Response]: C.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 877:  88%|▉| 878/999 [10
[Running Accuracy]: 0.5535,[Response]: A.<|endoftext|>, [Correct Ans]: More rich, , [Prog]: 878:  88%|▉| 878/999 [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>Compared to the first image, how is the color richness of 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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([-30.6406], 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([-31.2188], 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]: 0.5535,[Response]: A.<|endoftext|>, [Correct Ans]: More rich, , [Prog]: 878:  88%|▉| 879/999 [10:3
[Running Accuracy]: 0.5540,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 879:  88%|▉| 879/999 [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. 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>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([-31.0312], 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([-30.9219], 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]: 0.5540,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 879:  88%|▉| 880/999 [10
[Running Accuracy]: 0.5534,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 880:  88%|▉| 880/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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 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 sharpness of the second image higher than that of the first image?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the sharpness of the second image higher than that of the first image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
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])
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]: 0.5534,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 880:  88%|▉| 881/999 
[Running Accuracy]: 0.5528,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 881:  88%|▉| 881/999 [10:39<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 sharpness of the second image higher than that of 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 fidelity 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 fidelity 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 fidelity 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([-31.2344], 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])
 A.

[Running Accuracy]: 0.5528,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 881:  88%|▉| 882/999 [10:39<01:2
[Running Accuracy]: 0.5522,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 882:  88%|▉| 882/999 [10:39<01:22
 {'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>Is the fidelity 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>Which part below is most severely affected by overexposure?
A. The background light source in the second image
B. The monster's face 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 overexposure?
A. The background light source in the second image
B. The monster's face 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 overexposure?\nA. The background light source in the second image\nB. The monster's face 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([-31.2500], 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([-30.8906], 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]: 0.5522,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 882:  88%|▉| 883/999 [10:40<01:20
[Running Accuracy]: 0.5527,[Response]: A.<|endoftext|>, [Correct Ans]: The 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. The background light source in the second image\nB. The monster's face 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 below is most severely affected by overexposure?
A. Ground in the first image
B. Person in the second image
C. Light source on the right side 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. Ground in the first image
B. Person in the second image
C. Light source on the right side 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. Ground in the first image\nB. Person in the second image\nC. Light source on the right side of 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([-31.2656], 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]: 0.5527,[Response]: A.<|endoftext|>, [Correct Ans]: The background light source in the second image
[Running Accuracy]: 0.5532,[Response]: C.<|endoftext|>, [Correct Ans]: Light source on the right side of the second im
 {'prompt': "A chat between a curious user and an artificial 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 in the second image\nC. Light source on the right side 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>Is the sharpness of the first image higher 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 sharpness of the first image higher than 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 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([-31.0469], 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]: C.<|endoftext|>, [Correct Ans]: Light source on the right side of the second im
[Running Accuracy]: 0.5525,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 885:  89%|▉| 885/999 [10:41<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>Is the sharpness of the first image higher 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([-31.1719], 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([-30.9062], 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]: 0.5525,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 885:  89%|▉| 886/999 [10:42<01:23
[Running Accuracy]: 0.5519,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 886:  89%|▉| 886/999 [10:42<01:23
 {'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 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 blurry?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Are both of these images blurry?\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([-31.0469], 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]: 0.5519,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 886:  89%|▉| 887/999 [10:43<01:21
[Running Accuracy]: 0.5524,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 887:  89%|▉| 887/999 [10:43<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 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>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([-31.4375], 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([-30.8438], 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]: 0.5524,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 887:  89%|▉| 888/999 [10:44<01:22
[Running Accuracy]: 0.5518,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 888:  89%|▉| 888/999 [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 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 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([-31.0938], 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([-30.9062], 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]: 0.5518,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 888:  89%|▉| 889/999 [10
[Running Accuracy]: 0.5512,[Response]: A.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 889:  89%|▉| 889/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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([-31.0938], 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])
 A.

[Running Accuracy]: 0.5512,[Response]: A.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 889:  89%|▉| 890/999 
[Running Accuracy]: 0.5517,[Response]: A.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 890:  89%|▉| 890/999 [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>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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 second image
B. The ceiling in the first image
C. The right wall 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 second image
B. The ceiling in the first image
C. The right wall 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 second image\nB. The ceiling in the first image\nC. The right wall in the first 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([-30.9375], 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]: 0.5517,[Response]: A.<|endoftext|>, [Correct Ans]: Clearer, , [Prog]: 890:  89%|▉| 891/999 [10:46<
[Running Accuracy]: 0.5511,[Response]: B.<|endoftext|>, [Correct Ans]: The right wall in the first image, , [Prog]: 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 second image\nB. The ceiling in the first image\nC. The right wall 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. Sky in the second image
B. Broken bridge in the first image
C. Right side tree 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 below is most severely affected by overexposure?
A. Sky in the second image
B. Broken bridge in the first image
C. Right side tree leaves 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. Broken bridge in the first image\nC. Right side tree leaves 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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.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]: B.<|endoftext|>, [Correct Ans]: The right wall in the first image, , [Prog]: 89
[Running Accuracy]: 0.5516,[Response]: A.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 892:  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. Sky in the second image\nB. Broken bridge in the first image\nC. Right side tree 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>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([-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]: 0.5516,[Response]: A.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 892:  89%|▉|
[Running Accuracy]: 0.5510,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 893:  89%|▉| 893/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 taxi in the second image
B. The background woods in the second image
C. The monster 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 taxi in the second image
B. The background woods in the second image
C. The monster 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 taxi in the second image\nB. The background woods in the second image\nC. The monster in the first image\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([-31.3438], 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]: 0.5510,[Response]: A.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 893:  89%|▉| 894/999 
[Running Accuracy]: 0.5515,[Response]: A.<|endoftext|>, [Correct Ans]: The taxi in the second image, , [Prog]: 894:  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 affected by motion blur?\nA. The taxi in the second image\nB. The background woods in the second image\nC. The monster 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([-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([-30.7188], 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]: 0.5515,[Response]: A.<|endoftext|>, [Correct Ans]: The taxi in the second image, , [Prog]: 894:  9
[Running Accuracy]: 0.5508,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 895:  90%|▉| 895/999 [10:49<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 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([-31.3438], 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]: 0.5508,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 895:  90%|▉| 896/999 [10:50<01:1
[Running Accuracy]: 0.5513,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 896:  90%|▉| 896/999 [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. 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>Which part below has the most severe overexposure?
A. The light source part of the first image
B. The ground of the second image
C. The right side 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 has the most severe overexposure?
A. The light source part of the first image
B. The ground of the second image
C. The right side ground of 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 light source part of the first image\nB. The ground of the second image\nC. The right side ground of the first 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([-30.9375], 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]: 0.5513,[Response]: B.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 896:  90%|▉| 897/999 [10
[Running Accuracy]: 0.5518,[Response]: A.<|endoftext|>, [Correct Ans]: The light source part of 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 below has the most severe overexposure?\nA. The light source part of the first image\nB. The ground of the second image\nC. The right side 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>Compared to the first image, how is the illumination of the second 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:Compared to the first image, how is the illumination of the second image?
A. More sufficient
B. About the same
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. More sufficient\nB. About the same\nC. Less 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([-31.1250], 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]: 0.5518,[Response]: A.<|endoftext|>, [Correct Ans]: The light source part of the first image, , [Pr
[Running Accuracy]: 0.5523,[Response]: C.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 898:  90%|▉| 898/999
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 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 realism of 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 is the realism of 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 is the realism of 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([-31.0156], 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([-31.2500], 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]: 0.5523,[Response]: C.<|endoftext|>, [Correct Ans]: Less sufficient, , [Prog]: 898:  90%|▉| 899/999
[Running Accuracy]: 0.5517,[Response]: B.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 899:  90%|▉| 899/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. 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 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([-31.1406], 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])
 C.

[Running Accuracy]: 0.5517,[Response]: B.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 899:  90%|▉| 900/999 
[Running Accuracy]: 0.5522,[Response]: C.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 900:  90%|▉| 900/999 [10:53<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>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': '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. More Adequate
B. Less Adequate
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 of the second image?
A. More Adequate
B. Less Adequate
C. Similar
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. More Adequate\nB. Less Adequate\nC. Similar\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([-31.4375], 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]: 0.5522,[Response]: C.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 900:  90%|▉| 901/999 [10:54<0
[Running Accuracy]: 0.5516,[Response]: B.<|endoftext|>, [Correct Ans]: More Adequate, , [Prog]: 901:  90%|▉| 901/999 [
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. More Adequate\nB. Less Adequate\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 less clear 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 clear 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 clear than the second image?\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([-31.5469], 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]: 0.5516,[Response]: B.<|endoftext|>, [Correct Ans]: More Adequate, , [Prog]: 901:  90%|▉| 902/999 [
[Running Accuracy]: 0.5510,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 902:  90%|▉| 902/999 [10:54<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 less clear 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([-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([-30.9375], 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]: 0.5510,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 902:  90%|▉| 903/999 [10:55<01:0
[Running Accuracy]: 0.5515,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 903:  90%|▉| 903/999 [10:55<01:07
 {'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 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 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 images very clear?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are both 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([-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]: 0.5515,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 903:  90%|▉| 904/999 [10:56<01:04
[Running Accuracy]: 0.5520,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 904:  90%|▉| 904/999 [10:56<01:04
 {'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 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. Background of the second image
B. Doll in the first image
C. 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 part has the richest texture details?
A. Background of the second image
B. Doll in the first image
C. Dog 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 second image\nB. Doll in the first image\nC. Dog in the second image\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([-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]: 0.5520,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 904:  91%|▉| 905/999 [10:56<01:02
[Running Accuracy]: 0.5525,[Response]: C.<|endoftext|>, [Correct Ans]: Dog in the second image, , [Prog]: 905:  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 part has the richest texture details?\nA. Background of the second image\nB. Doll in the first image\nC. 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>Which part has the richest texture detail?
A. Background of the first image
B. Fox in the first image
C. Stamen 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 detail?
A. Background of the first image
B. Fox in the first image
C. Stamen of the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has the richest texture detail?\nA. Background of the first image\nB. Fox in the first image\nC. Stamen of 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([-31.5156], 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]: 0.5525,[Response]: C.<|endoftext|>, [Correct Ans]: Dog in the second image, , [Prog]: 905:  91%|▉|
[Running Accuracy]: 0.5530,[Response]: C.<|endoftext|>, [Correct Ans]: Stamen of the second image, , [Prog]: 906:  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 part has the richest texture detail?\nA. Background of the first image\nB. Fox in the first image\nC. Stamen 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>Is the first image less sharp 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 sharp 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 sharp than the second image?\nA. No\nB. Yes\nAnswer with the option's letter from the given choices directly.\n"]]
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])
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]: 0.5530,[Response]: C.<|endoftext|>, [Correct Ans]: Stamen of the second image, , [Prog]: 906:  91%
[Running Accuracy]: 0.5535,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 907:  91%|▉| 907/999 [10:57<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 first image less sharp 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. Desktop of the first image
B. Starfish of the first image
C. Upper left corner 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. Desktop of the first image
B. Starfish of the first image
C. Upper left corner 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. Desktop of the first image\nB. Starfish of the first image\nC. Upper left corner of 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([-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]: 0.5535,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 907:  91%|▉| 908/999 [10:58<00:5
[Running Accuracy]: 0.5529,[Response]: A.<|endoftext|>, [Correct Ans]: Upper 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 is most severely affected by overexposure?\nA. Desktop of the first image\nB. Starfish of the first image\nC. Upper left corner 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 person on the right side of the second image
B. The coral in the first image
C. The pedestrian on the left side 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 motion blur?
A. The person on the right side of the second image
B. The coral in the first image
C. The pedestrian on the left side of 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 on the right side of the second image\nB. The coral in the first image\nC. The pedestrian on the left side of 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([-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]: 0.5529,[Response]: A.<|endoftext|>, [Correct Ans]: Upper left corner of the second image, , [Prog]
[Running Accuracy]: 0.5534,[Response]: C.<|endoftext|>, [Correct Ans]: The pedestrian on the left side of the second 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 motion blur?\nA. The person on the right side of the second image\nB. The coral in the first image\nC. The pedestrian on the left side 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>Is the sharpness 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 sharpness 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 sharpness 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([-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([-30.7812], 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]: 0.5534,[Response]: C.<|endoftext|>, [Correct Ans]: The pedestrian on the left side of the second i
[Running Accuracy]: 0.5527,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 910:  91%|▉| 910/999 [10:59<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 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 background of the first image
B. The rider 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 below is most severely affected by motion blur?
A. The background of the first image
B. The rider 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 below is most severely affected by motion blur?\nA. The background of the first image\nB. The rider 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([-30.4688], 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]: 0.5527,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 910:  91%|▉| 911/999 [11:00<00:5
[Running Accuracy]: 0.5532,[Response]: A.<|endoftext|>, [Correct Ans]: The background of 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 part below is most severely affected by motion blur?\nA. The background of the first image\nB. The rider 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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 sea in the second image
B. The bird 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 severely affected by motion blur?
A. The sea in the second image
B. The bird 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 severely affected by motion blur?\nA. The sea in the second image\nB. The bird 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([-30.9375], 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]: 0.5532,[Response]: A.<|endoftext|>, [Correct Ans]: The background of the first image, , [Prog]: 91
[Running Accuracy]: 0.5526,[Response]: A.<|endoftext|>, [Correct Ans]: The background 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 part is most severely affected by motion blur?\nA. The sea in the second image\nB. The bird 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>Which part is most severely affected by motion blur?
A. The mountain in the second image
B. The vehicle 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 mountain in the second image
B. The vehicle 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 mountain in the second image\nB. The vehicle 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([-30.8438], 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([-31.3906], 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]: 0.5526,[Response]: A.<|endoftext|>, [Correct Ans]: The background in the first image, , [Prog]: 91
[Running Accuracy]: 0.5531,[Response]: B.<|endoftext|>, [Correct Ans]: The vehicle in the first image, , [Prog]: 913: 
 {'prompt': "A chat between a curious user and an artificial 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 mountain in the second image\nB. The vehicle 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>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([-30.7812], 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([-30.8125], 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]: 0.5531,[Response]: B.<|endoftext|>, [Correct Ans]: The vehicle in the first image, , [Prog]: 913: 
[Running Accuracy]: 0.5536,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 914:  91%|▉| 914/999 [11:02<00:54
 {'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 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 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([-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([-31.3281], 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]: 0.5536,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 914:  92%|▉| 915/999 [11:03<00:54
[Running Accuracy]: 0.5530,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 915:  92%|▉| 915/999 [11:03<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. 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 figure in the first image
B. The right sky in the second 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 below is most severely affected by overexposure?
A. The figure in the first image
B. The right sky in the second image
C. The water 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 overexposure?\nA. The figure in the first image\nB. The right sky in the second image\nC. The water surface 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([-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]: 0.5530,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 915:  92%|▉| 916/999 [11:03<00:5
[Running Accuracy]: 0.5535,[Response]: B.<|endoftext|>, [Correct Ans]: The right sky in the second 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 part below is most severely affected by overexposure?\nA. The figure in the first image\nB. The right sky in the second image\nC. The water surface 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 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 is 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 is 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([-31.3125], 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([-31.1250], 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]: 0.5535,[Response]: B.<|endoftext|>, [Correct Ans]: The right sky in the second image, , [Prog]: 91
[Running Accuracy]: 0.5529,[Response]: A.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 917:  92%|▉| 917/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 authentic\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>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([-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([-31.5156], 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]: 0.5529,[Response]: A.<|endoftext|>, [Correct Ans]: More authentic, , [Prog]: 917:  92%|▉| 918/999 
[Running Accuracy]: 0.5534,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 918:  92%|▉| 918/999 [11:05<00:59
 {'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 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 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([-31.4844], 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])
 B.

[Running Accuracy]: 0.5534,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 918:  92%|▉| 919/999 [11:06<00:56
[Running Accuracy]: 0.5528,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 919:  92%|▉| 919/999 [11:06<00:56
 {'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 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 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([-31.2500], 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([-31.2344], 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]: 0.5528,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 919:  92%|▉| 920/999 [11:06<00:54
[Running Accuracy]: 0.5533,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 920:  92%|▉| 920/999 [11:06<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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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([-30.8594], 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]: Yes, , [Prog]: 920:  92%|▉| 921/999 [11:07<00:5
[Running Accuracy]: 0.5527,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 921:  92%|▉| 921/999 [11:07<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 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. 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([-31.3125], 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([-30.2188], 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]: 0.5527,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 921:  92%|▉| 922/999 [11:08<00:5
[Running Accuracy]: 0.5531,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 922:  92%|▉| 922/999 [11:08<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 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 snowflake-like distortion?
A. Characters in the first image
B. Background of the first image
C. 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 snowflake-like distortion?
A. Characters in the first image
B. Background of the first image
C. 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 snowflake-like distortion?\nA. Characters in the first image\nB. Background of the first image\nC. Sky 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([-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]: 0.5531,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 922:  92%|▉| 923/999 [11:09<00:5
[Running Accuracy]: 0.5525,[Response]: B.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 923:  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 most severely affected by snowflake-like distortion?\nA. Characters in the first image\nB. Background of the first image\nC. 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>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([-30.8906], 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.], device='cuda:0', dtype=torch.float16)
Attn torch.Size([1, 729, 32])
vlm_prompt torch.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]: B.<|endoftext|>, [Correct Ans]: Sky in the second image, , [Prog]: 923:  92%|▉|
[Running Accuracy]: 0.5530,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 924:  92%|▉| 924/999 [11:09<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. 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 dog in the first image
B. the sky 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 has the richest texture details?
A. the dog in the first image
B. the sky 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 has the richest texture details?\nA. the dog in the first image\nB. the sky 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([-30.7500], 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([-30.8438], 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]: 0.5530,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 924:  93%|▉| 925/999 [11:10<00:50
[Running Accuracy]: 0.5535,[Response]: A.<|endoftext|>, [Correct Ans]: the dog in the first image, , [Prog]: 925:  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 has the richest texture details?\nA. the dog in the first image\nB. the sky 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>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([-31.2656], 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([-31.0156], 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]: 0.5535,[Response]: A.<|endoftext|>, [Correct Ans]: the dog in the first image, , [Prog]: 925:  93%
[Running Accuracy]: 0.5529,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 926:  93%|▉| 926/999 [11:11<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 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>Which part below is most severely affected by motion blur?
A. Characters in the second image
B. Characters in the first image
C. 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 below is most severely affected by motion blur?
A. Characters in the second image
B. Characters in the first image
C. Background 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. Characters in the second image\nB. Characters in the first image\nC. Background in the first 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([-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]: 0.5529,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 926:  93%|▉| 927/999 [11:11<00:4
[Running Accuracy]: 0.5523,[Response]: B.<|endoftext|>, [Correct Ans]: Characters in the second image, , [Prog]: 927: 
 {'prompt': "A chat between a curious user and an artificial 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. Characters in the second image\nB. Characters in the first image\nC. Background 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 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([-31.1875], 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])
 B.

[Running Accuracy]: 0.5523,[Response]: B.<|endoftext|>, [Correct Ans]: Characters in the second image, , [Prog]: 927: 
[Running Accuracy]: 0.5528,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 928:  93%|▉| 928/999 [11:12<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>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>Which part below is most severely affected by overexposure?
A. The tongue of the dog in the second image
B. The dog in the first image
C. The computer 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 tongue of the dog in the second image
B. The dog in the first image
C. The computer 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 tongue of the dog in the second image\nB. The dog in the first image\nC. The computer 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([-30.9375], 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]: 0.5528,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 928:  93%|▉| 929/999 [11:13<00:4
[Running Accuracy]: 0.5533,[Response]: A.<|endoftext|>, [Correct Ans]: The tongue of the dog 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 below is most severely affected by overexposure?\nA. The tongue of the dog in the second image\nB. The dog in the first image\nC. The computer 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 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([-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])
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]: 0.5533,[Response]: A.<|endoftext|>, [Correct Ans]: The tongue of the dog in the second image, , [P
[Running Accuracy]: 0.5538,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 930:  93%|▉| 930/999 [11:13<00:47
 {'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>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 has the richest texture details?
A. Background of the first image
B. Insect 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 has the richest texture details?
A. Background of the first image
B. Insect in the second image
C. 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. Background of the first image\nB. Insect in the second image\nC. Background of the second image\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-31.0469], 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])
 B.

[Running Accuracy]: 0.5538,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 930:  93%|▉| 931/999 [11:14<00:46
[Running Accuracy]: 0.5542,[Response]: B.<|endoftext|>, [Correct Ans]: Insect in the second image, , [Prog]: 931:  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 has the richest texture details?\nA. Background of the first image\nB. Insect 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>Which part is most severely affected by out-of-focus?
A. Background of the first image
B. Handlebar of the first image's bicycle
C. Guitar 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 out-of-focus?
A. Background of the first image
B. Handlebar of the first image's bicycle
C. Guitar in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part is most severely affected by out-of-focus?\nA. Background of the first image\nB. Handlebar of the first image's bicycle\nC. Guitar 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([-31.2812], 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]: 0.5542,[Response]: B.<|endoftext|>, [Correct Ans]: Insect in the second image, , [Prog]: 931:  93%
[Running Accuracy]: 0.5547,[Response]: A.<|endoftext|>, [Correct Ans]: Background of the first image, , [Prog]: 932:  
 {'prompt': "A chat between a curious user and an artificial 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 out-of-focus?\nA. Background of the first image\nB. Handlebar of the first image's bicycle\nC. Guitar 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. 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([-31.2969], 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([-30.9844], 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]: 0.5547,[Response]: A.<|endoftext|>, [Correct Ans]: Background of the first image, , [Prog]: 932:  
[Running Accuracy]: 0.5541,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 933:  93%|▉| 933/999 [11:15<00:43
 {'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 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 figures in the first image
B. The glass 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 figures in the first image
B. The glass 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 figures in the first image\nB. The glass 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([-31.3281], 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([-31.3281], 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]: 0.5541,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 933:  93%|▉| 934/999 [11:16<00:43
[Running Accuracy]: 0.5546,[Response]: C.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 934:  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 figures in the first image\nB. The glass 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>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([-30.8281], 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]: 0.5546,[Response]: C.<|endoftext|>, [Correct Ans]: The sky in the second image, , [Prog]: 934:  94
[Running Accuracy]: 0.5540,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 935:  94%|▉| 935/999 [11:17<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. 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([-30.9062], 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([-30.7656], 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]: 0.5540,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 935:  94%|▉| 936/999 [11:17<00:4
[Running Accuracy]: 0.5545,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 936:  94%|▉| 936/999 [11:17<00:42
 {'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 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 the texture details of these two images relatively 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 relatively 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 relatively rich?\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([-31.1094], 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]: 0.5545,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 936:  94%|▉| 937/999 [11:18<00:41
[Running Accuracy]: 0.5550,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 937:  94%|▉| 937/999 [11:18<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>Are the texture details of these two images relatively rich?\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. More blurry
B. Less clear
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. Less clear
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. Less clear\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([-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]: 0.5550,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 937:  94%|▉| 938/999 [11:19<00:4
[Running Accuracy]: 0.5554,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 938:  94%|▉| 938/999 [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. More blurry\nB. Less clear\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 characters in the first image
B. The background in the first image
C. The right-side 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 characters in the first image
B. The background in the first image
C. The right-side 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 characters in the first image\nB. The background in the first image\nC. The right-side wall 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([-30.9688], 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]: 0.5554,[Response]: A.<|endoftext|>, [Correct Ans]: More blurry, , [Prog]: 938:  94%|▉| 939/999 [11
[Running Accuracy]: 0.5548,[Response]: B.<|endoftext|>, [Correct Ans]: The right-side wall 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 overexposure?\nA. The characters in the first image\nB. The background in the first image\nC. The right-side 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>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([-31.0781], 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([-30.7656], 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]: 0.5548,[Response]: B.<|endoftext|>, [Correct Ans]: The right-side wall in the second image, , [Pro
[Running Accuracy]: 0.5543,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 940:  94%|▉| 940/999 [11:21<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 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>Compared to the first image, how is the sharpness 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 sharpness 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 sharpness of the second image?\nA. Similar\nB. Clearer\nC. Blurrier\nAnswer with the option's letter from the given choices directly.\n"]]
alpha tensor([-30.5625], 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([-31.2812], 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]: 0.5543,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 940:  94%|▉| 941/999 [11:21<00:43
[Running Accuracy]: 0.5547,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 941:  94%|▉| 941/999 [11:21
 {'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 sharpness 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>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([-31.2969], 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([-31.1562], 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]: 0.5547,[Response]: C.<|endoftext|>, [Correct Ans]: Blurrier, , [Prog]: 941:  94%|▉| 942/999 [11:22
[Running Accuracy]: 0.5552,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 942:  94%|▉| 942/999 [11:22<00:40
 {'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>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 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([-31.0625], 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]: 0.5552,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 942:  94%|▉| 943/999 [11:22<00:37
[Running Accuracy]: 0.5546,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 943:  94%|▉| 943/999 [11:22<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 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 texture detail of the second image?
A. less rich
B. 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 texture detail of the second image?
A. less rich
B. 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 texture detail of the second image?\nA. less rich\nB. richer\nC. about the same\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([-30.7656], 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]: 0.5546,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 943:  94%|▉| 944/999 [11:23<00:4
[Running Accuracy]: 0.5551,[Response]: A.<|endoftext|>, [Correct Ans]: less rich, , [Prog]: 944:  94%|▉| 944/999 [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>Compared to the first image, how is the texture detail of the second image?\nA. less rich\nB. 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>Is the texture detail of the first image less rich 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 less rich 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 less rich 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([-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]: 0.5551,[Response]: A.<|endoftext|>, [Correct Ans]: less rich, , [Prog]: 944:  95%|▉| 945/999 [11:2
[Running Accuracy]: 0.5545,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 945:  95%|▉| 945/999 [11:24<00:40
 {'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 texture detail of the first image less rich 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([-31.2812], 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([-31.0469], 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]: 0.5545,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 945:  95%|▉| 946/999 [11:25<00:37
[Running Accuracy]: 0.5539,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 946:  95%|▉| 946/999 [11:25<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>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 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([-30.7500], 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([-30.8438], 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]: 0.5539,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 946:  95%|▉| 947/999 [11:25<00:3
[Running Accuracy]: 0.5544,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 947:  95%|▉| 947/999 [11:25<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>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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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. 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 weak in both of these images?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Are the lighting conditions weak in both of these images?\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([-31.2812], 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]: 0.5544,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 947:  95%|▉| 948/999 [11:26<00:38
[Running Accuracy]: 0.5549,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 948:  95%|▉| 948/999 [11:26<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 the lighting conditions weak 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>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([-30.8906], 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([-31.4219], 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]: 0.5549,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 948:  95%|▉| 949/999 [11:27<00:4
[Running Accuracy]: 0.5553,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 949:  95%|▉| 949/999 [11:27<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 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>Compared to the first image, how is the lighting in the second image?
A. Much worse
B. Much much worse
C. About the same
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 lighting in the second image?
A. Much worse
B. Much much worse
C. About the same
D. Much better
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 much worse\nC. About the same\nD. Much better\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([-31.7344], 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])
 D.

[Running Accuracy]: 0.5553,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 949:  95%|▉| 950/999 [11:28<00:4
[Running Accuracy]: 0.5558,[Response]: D.<|endoftext|>, [Correct Ans]: Much better, , [Prog]: 950:  95%|▉| 950/999 [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 lighting in the second image?\nA. Much worse\nB. Much much worse\nC. About the same\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>Have both of these images been affected by blurring?
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 been affected by blurring?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Have both of these images been affected by blurring?\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([-30.7656], 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]: 0.5558,[Response]: D.<|endoftext|>, [Correct Ans]: Much better, , [Prog]: 950:  95%|▉| 951/999 [11
[Running Accuracy]: 0.5563,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 951:  95%|▉| 951/999 [11:29<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>Have both of these images been affected by blurring?\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 composition of the second image?
A. Much worse
B. Much better
C. About the same
D. A lot 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. Much worse
B. Much better
C. About the same
D. A lot 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. Much worse\nB. Much better\nC. About the same\nD. A lot 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([-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]: 0.5563,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 951:  95%|▉| 952/999 [11:30<00:4
[Running Accuracy]: 0.5567,[Response]: B.<|endoftext|>, [Correct Ans]: Much better, , [Prog]: 952:  95%|▉| 952/999 [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 composition of the second image?\nA. Much worse\nB. Much better\nC. About the same\nD. A lot 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>Which image is 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 is more realistic?
A. the first image
B. the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which image is more realistic?\nA. the first image\nB. 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([-30.7188], 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]: 0.5567,[Response]: B.<|endoftext|>, [Correct Ans]: Much better, , [Prog]: 952:  95%|▉| 953/999 [11
[Running Accuracy]: 0.5572,[Response]: A.<|endoftext|>, [Correct Ans]: the first image, , [Prog]: 953:  95%|▉| 953/999
 {'prompt': "A chat between a curious user and an artificial 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 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>Compared to the second image, how would you describe the richness of colors in the first image?
A. Richer
B. Similar
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 second image, how would you describe the richness of colors in the first image?
A. Richer
B. Similar
C. More monotonous
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. Richer\nB. Similar\nC. More monotonous\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([-31.2812], 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]: 0.5572,[Response]: A.<|endoftext|>, [Correct Ans]: the first image, , [Prog]: 953:  95%|▉| 954/999
[Running Accuracy]: 0.5577,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 954:  95%|▉| 954/999 [11:32<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 second image, how would you describe the richness of colors in the first image?\nA. Richer\nB. Similar\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 composition of the second image?
A. Much better
B. Much worse
C. About the same
D. 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 composition of the second image?
A. Much better
B. Much worse
C. About the same
D. Much 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. Much better\nB. Much worse\nC. About the same\nD. Much worse\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([-31.3438], 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]: 0.5577,[Response]: A.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 954:  96%|▉| 955/999 [11:33<0
[Running Accuracy]: 0.5581,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 955:  96%|▉| 955/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 better\nB. Much worse\nC. About the same\nD. 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>Is the sharpness 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 sharpness 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 sharpness 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([-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]: 0.5581,[Response]: C.<|endoftext|>, [Correct Ans]: About the same, , [Prog]: 955:  96%|▉| 956/999 
[Running Accuracy]: 0.5575,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 956:  96%|▉| 956/999 [11:34<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 sharpness 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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([-31.1875], 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([-31.3281], 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]: 0.5575,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 956:  96%|▉| 957/999 [11:35<00:4
[Running Accuracy]: 0.5580,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 957:  96%|▉| 957/999 [11:35<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 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>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([-31.1094], 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])
 A.

[Running Accuracy]: 0.5580,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 957:  96%|▉| 958/999 [11:36<00:3
[Running Accuracy]: 0.5585,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 958:  96%|▉| 958/999 [11:36<00:38
 {'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 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>Is the color of the second image more vibrant?
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 more vibrant?
A. No
B. Yes
Answer with the option's letter from the given choices directly.

prompts: [["Is the color of the second image more vibrant?\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([-31.3281], 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]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 958:  96%|▉| 959/999 [11:37<00:34
[Running Accuracy]: 0.5589,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 959:  96%|▉| 959/999 [11:37<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 color of the second image more vibrant?\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 sharpness of the first image?
A. Much lower
B. Almost the same
C. Much lower
D. Much 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 is the sharpness of the first image?
A. Much lower
B. Almost the same
C. Much lower
D. Much higher
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. Much lower\nB. Almost the same\nC. Much lower\nD. Much higher\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([-31.5312], 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]: 0.5589,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 959:  96%|▉| 960/999 [11:37<00:3
[Running Accuracy]: 0.5583,[Response]: A.<|endoftext|>, [Correct Ans]: Much higher, , [Prog]: 960:  96%|▉| 960/999 [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 sharpness of the first image?\nA. Much lower\nB. Almost the same\nC. Much lower\nD. Much 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>Which image has more obvious 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 obvious overexposure?
A. First image
B. Second image
Answer with the option's letter from the given choices directly.

prompts: [["Which image has more obvious overexposure?\nA. First image\nB. 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([-30.7188], 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]: 0.5583,[Response]: A.<|endoftext|>, [Correct Ans]: Much higher, , [Prog]: 960:  96%|▉| 961/999 [11
[Running Accuracy]: 0.5588,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 961:  96%|▉| 961/999 [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>Which image has more obvious 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 clearer?
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 clearer?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.

prompts: [["Which image is clearer?\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([-31.0156], 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]: 0.5588,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 961:  96%|▉| 962/999 [11
[Running Accuracy]: 0.5593,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 962:  96%|▉| 962/999 [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 clearer?\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 higher image sharpness?
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 image sharpness?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.

prompts: [["Which image has higher image sharpness?\nA. Second image\nB. First 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([-31.1875], 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]: 0.5593,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 962:  96%|▉| 963/999 [1
[Running Accuracy]: 0.5597,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 963:  96%|▉| 963/999 [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 image sharpness?\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 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([-30.9375], 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([-31.1094], 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]: 0.5597,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 963:  96%|▉| 964/999 [1
[Running Accuracy]: 0.5602,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 964:  96%|▉| 964/999 [11:40<00:25
 {'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 of the following types of distortion did not appear in the two images?
A. Noise
B. Overexposure
C. Blur
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 types of distortion did not appear in the two images?
A. Noise
B. Overexposure
C. Blur
D. Underexposure
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following types of distortion did not appear in the two images?\nA. Noise\nB. Overexposure\nC. Blur\nD. Underexposure\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([-31.2344], 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]: 0.5602,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 964:  97%|▉| 965/999 [11:41<00:24
[Running Accuracy]: 0.5596,[Response]: D.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 965:  97%|▉| 965/999 [11:41<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 types of distortion did not appear in the two images?\nA. Noise\nB. Overexposure\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 second image, how is the sharpness of the first image?
A. Much worse
B. About 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 second image, how is the sharpness of the first image?
A. Much worse
B. About the same
C. Much worse
D. Much better
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. Much worse\nB. About the same\nC. Much worse\nD. Much better\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([-30.9531], 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]: 0.5596,[Response]: D.<|endoftext|>, [Correct Ans]: Noise, , [Prog]: 965:  97%|▉| 966/999 [11:42<00
[Running Accuracy]: 0.5590,[Response]: A.<|endoftext|>, [Correct Ans]: Much better, , [Prog]: 966:  97%|▉| 966/999 [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 sharpness of the first image?\nA. Much worse\nB. About the same\nC. Much worse\nD. 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>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([-30.8438], 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([-31.0625], 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]: 0.5590,[Response]: A.<|endoftext|>, [Correct Ans]: Much better, , [Prog]: 966:  97%|▉| 967/999 [11
[Running Accuracy]: 0.5595,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 967:  97%|▉| 967/999 [11:43<00:23
 {'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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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 more rich and vibrant 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 more rich and vibrant 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 more rich and vibrant than the first image?\nA. Yes\nB. No\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([-31.0938], 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]: 0.5595,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 967:  97%|▉| 968/999 [11:44<00:27
[Running Accuracy]: 0.5599,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 968:  97%|▉| 968/999 [11:44<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 the color of the second image more rich and vibrant 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 more aesthetically pleasing 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 aesthetically pleasing 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 aesthetically pleasing than the first 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([-31.3125], 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]: 0.5599,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 968:  97%|▉| 969/999 [11:45<00:2
[Running Accuracy]: 0.5593,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 969:  97%|▉| 969/999 [11:45<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 the second image more aesthetically pleasing 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 first image, how is the lighting in the second image?
A. much worse
B. much much worse
C. much better
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. much worse
B. much much worse
C. much better
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. much worse\nB. much much worse\nC. much better\nD. 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([-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]: 0.5593,[Response]: A.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 969:  97%|▉| 970/999 [11:45<00:2
[Running Accuracy]: 0.5598,[Response]: C.<|endoftext|>, [Correct Ans]: much better, , [Prog]: 970:  97%|▉| 970/999 [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 lighting in the second image?\nA. much worse\nB. much much worse\nC. much better\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>Which image has a more realistic style?
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 a more realistic style?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.

prompts: [["Which image has a more realistic style?\nA. Second image\nB. 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([-31.2500], 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]: 0.5598,[Response]: C.<|endoftext|>, [Correct Ans]: much better, , [Prog]: 970:  97%|▉| 971/999 [11
[Running Accuracy]: 0.5592,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 971:  97%|▉| 971/999 [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>Which image has a more realistic style?\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>What distortion does not appear in these two images?
A. Poor composition
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 does not appear in these two images?
A. Poor composition
B. Noise
C. Motion blur
D. Underexposure
Answer with the option's letter from the given choices directly.

prompts: [["What distortion does not appear in these two images?\nA. Poor composition\nB. Noise\nC. Motion blur\nD. Underexposure\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([-30.9375], 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]: 0.5592,[Response]: A.<|endoftext|>, [Correct Ans]: First image, , [Prog]: 971:  97%|▉| 972/999 [11
[Running Accuracy]: 0.5586,[Response]: D.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 972:  97%|▉| 972/999 [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>What distortion does not appear in these two images?\nA. Poor composition\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>Which part has suffered the most severe overexposure?
A. The signboard in the second image
B. The phone in the first image
C. The background in the first image
D. The roof 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 suffered the most severe overexposure?
A. The signboard in the second image
B. The phone in the first image
C. The background in the first image
D. The roof in the second image
Answer with the option's letter from the given choices directly.

prompts: [["Which part has suffered the most severe overexposure?\nA. The signboard in the second image\nB. The phone in the first image\nC. The background in the first image\nD. The roof 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([-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]: 0.5586,[Response]: D.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 972:  97%|▉| 973/999 [11
[Running Accuracy]: 0.5591,[Response]: C.<|endoftext|>, [Correct Ans]: The background in the first image, , [Prog]: 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>Which part has suffered the most severe overexposure?\nA. The signboard in the second image\nB. The phone in the first image\nC. The background in the first image\nD. The roof 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 image has richer texture details?
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 richer texture details?
A. Second image
B. First image
Answer with the option's letter from the given choices directly.

prompts: [["Which image has richer texture details?\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([-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]: 0.5591,[Response]: C.<|endoftext|>, [Correct Ans]: The background in the first image, , [Prog]: 97
[Running Accuracy]: 0.5595,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 974:  97%|▉| 974/999 [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 richer texture details?\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 color vividness of the second image?
A. More vivid
B. Almost 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 is the color vividness of the second image?
A. More vivid
B. Almost the same
C. More monotonous
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. Almost the same\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([-31.2500], 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]: 0.5595,[Response]: A.<|endoftext|>, [Correct Ans]: Second image, , [Prog]: 974:  98%|▉| 975/999 [1
[Running Accuracy]: 0.5600,[Response]: A.<|endoftext|>, [Correct Ans]: More vivid, , [Prog]: 975:  98%|▉| 975/999 [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 color vividness of the second image?\nA. More vivid\nB. Almost 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 aesthetic appeal of the second image?
A. Similar
B. Less appealing
C. More appealing
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 appeal of the second image?
A. Similar
B. Less appealing
C. More appealing
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the first image, how is the aesthetic appeal of the second image?\nA. Similar\nB. Less appealing\nC. More appealing\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([-30.5781], 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]: 0.5600,[Response]: A.<|endoftext|>, [Correct Ans]: More vivid, , [Prog]: 975:  98%|▉| 976/999 [11:
[Running Accuracy]: 0.5594,[Response]: B.<|endoftext|>, [Correct Ans]: More appealing, , [Prog]: 976:  98%|▉| 976/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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 appeal of the second image?\nA. Similar\nB. Less appealing\nC. More appealing\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. 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([-31.2500], 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]: 0.5594,[Response]: B.<|endoftext|>, [Correct Ans]: More appealing, , [Prog]: 976:  98%|▉| 977/999 
[Running Accuracy]: 0.5589,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 977:  98%|▉| 977/999 [11:50<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 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 clarity of the second image?
A. A little worse
B. About the same
C. Slightly 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 clarity of the second image?
A. A little worse
B. About the same
C. Slightly better
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. A little worse\nB. About the same\nC. Slightly better\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([-31.4375], 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]: Yes, , [Prog]: 977:  98%|▉| 978/999 [11:51<00:1
[Running Accuracy]: 0.5593,[Response]: A.<|endoftext|>, [Correct Ans]: A little worse, , [Prog]: 978:  98%|▉| 978/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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. A little worse\nB. About the same\nC. Slightly 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>Is the texture detail 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 texture detail 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 texture detail 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([-31.1719], 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]: 0.5593,[Response]: A.<|endoftext|>, [Correct Ans]: A little worse, , [Prog]: 978:  98%|▉| 979/999 
[Running Accuracy]: 0.5598,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 979:  98%|▉| 979/999 [11:51<00:14
 {'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 texture detail 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': 'A.<|endoftext|>'} 

prompt A chat between a curious user and an artificial 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([-30.6094], 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([-30.5938], 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]: 0.5598,[Response]: A.<|endoftext|>, [Correct Ans]: No, , [Prog]: 979:  98%|▉| 980/999 [11:52<00:13
[Running Accuracy]: 0.5602,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 980:  98%|▉| 980/999 [11:52<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>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 is the composition of the second image?
A. Much better
B. Almost the same
C. Much worse
D. 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 first image, how is the composition of the second image?
A. Much better
B. Almost the same
C. Much worse
D. Slightly 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. Much better\nB. Almost the same\nC. Much worse\nD. Slightly worse\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([-31.2500], 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]: 0.5602,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 980:  98%|▉| 981/999 [11:53<00:13
[Running Accuracy]: 0.5596,[Response]: C.<|endoftext|>, [Correct Ans]: Much better, , [Prog]: 981:  98%|▉| 981/999 [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 composition of the second image?\nA. Much better\nB. Almost the same\nC. Much worse\nD. 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 is present in both of these images?
A. Underexposure
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:What kind of distortion is present in both of these images?
A. Underexposure
B. Motion blur
C. Overexposure
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion is present in both of these images?\nA. Underexposure\nB. Motion blur\nC. Overexposure\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([-31.0156], 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]: 0.5596,[Response]: C.<|endoftext|>, [Correct Ans]: Much better, , [Prog]: 981:  98%|▉| 982/999 [11
[Running Accuracy]: 0.5601,[Response]: A.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 982:  98%|▉| 982/999 [
 {'prompt': "A chat between a curious user and an artificial 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 present in both of these images?\nA. Underexposure\nB. Motion blur\nC. 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 slightly 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 slightly 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 slightly clearer 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([-30.2656], 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]: 0.5601,[Response]: A.<|endoftext|>, [Correct Ans]: Underexposure, , [Prog]: 982:  98%|▉| 983/999 [
[Running Accuracy]: 0.5595,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 983:  98%|▉| 983/999 [11:54<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 first image slightly 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>Which of the following distortions did not appear in the two images?
A. Noise
B. Underexposure
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:Which of the following distortions did not appear in the two images?
A. Noise
B. Underexposure
C. Low light
D. Out of focus
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. Underexposure\nC. Low light\nD. Out of focus\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([-30.9219], 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]: 0.5595,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 983:  98%|▉| 984/999 [11:55<00:1
[Running Accuracy]: 0.5589,[Response]: B.<|endoftext|>, [Correct Ans]: Out of focus, , [Prog]: 984:  98%|▉| 984/999 [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. Underexposure\nC. Low light\nD. 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>What problems are not present in the two images?
A. overexposure
B. motion blur
C. blur
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 problems are not present in the two images?
A. overexposure
B. motion blur
C. blur
D. out of focus
Answer with the option's letter from the given choices directly.

prompts: [["What problems are not present in the two images?\nA. overexposure\nB. motion blur\nC. blur\nD. out of focus\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([-30.7969], 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]: 0.5589,[Response]: B.<|endoftext|>, [Correct Ans]: Out of focus, , [Prog]: 984:  99%|▉| 985/999 [1
[Running Accuracy]: 0.5584,[Response]: A.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 985:  99%|▉| 985/999 [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>What problems are not present in the two images?\nA. overexposure\nB. motion blur\nC. blur\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 second image, what is the more severe distortion suffered by the first image?
A. Underexposure
B. Blurry
C. Low light
D. Snowflake
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 is the more severe distortion suffered by the first image?
A. Underexposure
B. Blurry
C. Low light
D. Snowflake
Answer with the option's letter from the given choices directly.

prompts: [["Compared to the second image, what is the more severe distortion suffered by the first image?\nA. Underexposure\nB. Blurry\nC. Low light\nD. Snowflake\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])
 D.

[Running Accuracy]: 0.5584,[Response]: A.<|endoftext|>, [Correct Ans]: motion blur, , [Prog]: 985:  99%|▉| 986/999 [11
[Running Accuracy]: 0.5588,[Response]: D.<|endoftext|>, [Correct Ans]: Snowflake, , [Prog]: 986:  99%|▉| 986/999 [11:5
 {'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, what is the more severe distortion suffered by the first image?\nA. Underexposure\nB. Blurry\nC. Low light\nD. Snowflake\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 of the following is not a problem that is more severe in the first image than in the second image?
A. Low light
B. Noise
C. Artifacts
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 is not a problem that is more severe in the first image than in the second image?
A. Low light
B. Noise
C. Artifacts
D. Motion blur
Answer with the option's letter from the given choices directly.

prompts: [["Which of the following is not a problem that is more severe in the first image than in the second image?\nA. Low light\nB. Noise\nC. Artifacts\nD. Motion blur\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([-30.9062], 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]: 0.5588,[Response]: D.<|endoftext|>, [Correct Ans]: Snowflake, , [Prog]: 986:  99%|▉| 987/999 [11:5
[Running Accuracy]: 0.5583,[Response]: A.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 987:  99%|▉| 987/999 [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>Which of the following is not a problem that is more severe in the first image than in the second image?\nA. Low light\nB. Noise\nC. Artifacts\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>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([-31.1406], 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([-31.1406], 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]: 0.5583,[Response]: A.<|endoftext|>, [Correct Ans]: Motion blur, , [Prog]: 987:  99%|▉| 988/999 [11
[Running Accuracy]: 0.5587,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 988:  99%|▉| 988/999 [11:58<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 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 image is more severely affected by motion blur?
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 is more severely affected by motion blur?
A. The second image
B. The first image
Answer with the option's letter from the given choices directly.

prompts: [["Which image is more severely affected by motion blur?\nA. The second image\nB. 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([-31.0938], 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]: 0.5587,[Response]: B.<|endoftext|>, [Correct Ans]: No, , [Prog]: 988:  99%|▉| 989/999 [11:58<00:06
[Running Accuracy]: 0.5581,[Response]: B.<|endoftext|>, [Correct Ans]: The second image, , [Prog]: 989:  99%|▉| 989/99
 {'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 image is more severely affected by motion blur?\nA. The second image\nB. 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 motion blur?
A. The kitten in the first image
B. The hand in the second image
C. The person's face 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 kitten in the first image
B. The hand in the second image
C. The person's face 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 kitten in the first image\nB. The hand in the second image\nC. The person's face 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([-30.6562], 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]: 0.5581,[Response]: B.<|endoftext|>, [Correct Ans]: The second image, , [Prog]: 989:  99%|▉| 990/99
[Running Accuracy]: 0.5586,[Response]: B.<|endoftext|>, [Correct Ans]: The hand in the second image, , [Prog]: 990:  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 motion blur?\nA. The kitten in the first image\nB. The hand in the second image\nC. The person's face 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. Less vivid
B. Similar
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. Similar
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. Similar\nC. More vivid\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([-31.5781], 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]: 0.5586,[Response]: B.<|endoftext|>, [Correct Ans]: The hand in the second image, , [Prog]: 990:  9
[Running Accuracy]: 0.5590,[Response]: C.<|endoftext|>, [Correct Ans]: More vivid, , [Prog]: 991:  99%|▉| 991/999 [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 color vividness of the second image?\nA. Less vivid\nB. Similar\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>What kind of distortion is not present in these two images?
A. Blur
B. Ghosting
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 is not present in these two images?
A. Blur
B. Ghosting
C. Out-of-focus
Answer with the option's letter from the given choices directly.

prompts: [["What kind of distortion is not present in these two images?\nA. Blur\nB. Ghosting\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([-31.1250], 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]: 0.5590,[Response]: C.<|endoftext|>, [Correct Ans]: More vivid, , [Prog]: 991:  99%|▉| 992/999 [12:
[Running Accuracy]: 0.5585,[Response]: A.<|endoftext|>, [Correct Ans]: Ghosting, , [Prog]: 992:  99%|▉| 992/999 [12:01
 {'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 kind of distortion is not present in these two images?\nA. Blur\nB. Ghosting\nC. 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 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([-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])
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])
 C.

[Running Accuracy]: 0.5585,[Response]: A.<|endoftext|>, [Correct Ans]: Ghosting, , [Prog]: 992:  99%|▉| 993/999 [12:01
[Running Accuracy]: 0.5589,[Response]: C.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 993:  99%|▉| 993/999 [12:01<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>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 texture detail of the second image?
A. Less rich
B. 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 texture detail of the second image?
A. Less rich
B. 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 texture detail of the second image?\nA. Less rich\nB. Richer\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([-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]: 0.5589,[Response]: C.<|endoftext|>, [Correct Ans]: Richer, , [Prog]: 993:  99%|▉| 994/999 [12:02<0
[Running Accuracy]: 0.5594,[Response]: A.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 994:  99%|▉| 994/999 [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. Less rich\nB. 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>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([-31.1719], 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([-30.3750], 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]: 0.5594,[Response]: A.<|endoftext|>, [Correct Ans]: Less rich, , [Prog]: 994: 100%|▉| 995/999 [12:0
[Running Accuracy]: 0.5588,[Response]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 995: 100%|▉| 995/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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>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([-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([-31.2188], 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]: C.<|endoftext|>, [Correct Ans]: More realistic, , [Prog]: 995: 100%|▉| 996/999 
[Running Accuracy]: 0.5592,[Response]: C.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 996: 100%|▉| 996/999 
 {'prompt': "A chat between a curious user and an artificial intelligence assistant. The 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': '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 figures in the second image
B. The upper right corner of the sky 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 figures in the second image
B. The upper right corner of the sky 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 figures in the second image\nB. The upper right corner of the sky 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([-31.5625], 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([-31.3594], 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]: 0.5592,[Response]: C.<|endoftext|>, [Correct Ans]: Less realistic, , [Prog]: 996: 100%|▉| 997/999 
[Running Accuracy]: 0.5597,[Response]: B.<|endoftext|>, [Correct Ans]: The upper right corner of the sky in 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 is most severely affected by overexposure?\nA. The figures in the second image\nB. The upper right corner of the sky 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': 'B.<|endoftext|>'} 

prompt A chat between a curious user and an artificial intelligence assistant. The 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([-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([-30.7500], 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])
 B.

[Running Accuracy]: 0.5597,[Response]: B.<|endoftext|>, [Correct Ans]: The upper right corner of the sky in the second
[Running Accuracy]: 0.5601,[Response]: B.<|endoftext|>, [Correct Ans]: less rich, , [Prog]: 998: 100%|▉| 998/999 [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. 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>Is the authenticity of the first image higher 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 authenticity of the first image higher than the second image?
A. Yes
B. No
Answer with the option's letter from the given choices directly.

prompts: [["Is the authenticity of the first image higher 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([-31.5625], 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])
 B.

[Running Accuracy]: 0.5601,[Response]: B.<|endoftext|>, [Correct Ans]: less rich, , [Prog]: 998: 100%|█| 999/999 [12:0
[Running Accuracy]: 0.5596,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 999: 100%|█| 999/999 [12:06<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 authenticity of the first image higher than the second image?\nA. Yes\nB. No\nAnswer with the option's letter from the given choices directly.\n ASSISTANT:", 'outputs': 'B.<|endoftext|>'} 


[Running Accuracy]: 0.5596,[Response]: B.<|endoftext|>, [Correct Ans]: Yes, , [Prog]: 999: 100%|█| 999/999 [12:06<00:0