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  1. checkpoints/finetune_lora_task33_2000step/checkpoints/steps_1000_pytorch_model.pt +3 -0
  2. checkpoints/finetune_lora_task33_2000step/checkpoints/steps_1500_pytorch_model.pt +3 -0
  3. checkpoints/finetune_lora_task33_2000step/checkpoints/steps_2000_pytorch_model.pt +3 -0
  4. checkpoints/finetune_lora_task33_2000step/checkpoints/steps_500_pytorch_model.pt +3 -0
  5. checkpoints/finetune_lora_task33_2000step/config.yaml +58 -0
  6. checkpoints/finetune_lora_task33_2000step/dataset_statistics.json +133 -0
  7. checkpoints/finetune_lora_task33_2000step/final_model/pytorch_model.pt +3 -0
  8. checkpoints/finetune_lora_task33_2000step/summary.jsonl +4 -0
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+ datasets:
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+ vla_data:
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+ CoT_prompt: Your task is {instruction}. To identify the key objects for your task.
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+ Locate their bounding boxes in [x1,y1,x2,y2] format.
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+ data_mix: libero_90_task_33
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+ data_root_dir: playground/Datasets/LEROBOT_LIBERO_DATA
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+ dataset_py: lerobot_datasets
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+ per_device_batch_size: 1
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+ sequential_step_sampling: false
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+ video_backend: torchvision_av
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+ framework:
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+ action_model:
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+ action_dim: 7
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+ future_action_window_size: 7
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+ past_action_window_size: 0
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+ name: QwenFast
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+ qwenvl:
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+ base_vlm: playground/Pretrained_models/Qwen2.5-VL-3B-Instruct-Action
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+ lora_config:
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+ lora_alpha: 32
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+ lora_dropout: 0.05
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+ r: 16
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+ target_modules:
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+ - q_proj
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+ - v_proj
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+ - k_proj
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+ - o_proj
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+ output_dir: ./results/Checkpoints/finetune_lora_task33_2000step
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+ run_id: finetune_lora_task33_2000step
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+ run_root_dir: ./results/Checkpoints
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+ seed: 42
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+ trainer:
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+ eval_interval: 100
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+ freeze_modules: qwen_vl_interface.model.model.visual,dino_encoder
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+ gradient_accumulation_steps: 1
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+ gradient_clipping: 1.0
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+ is_resume: true
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+ learning_rate:
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+ action_model: 0.0001
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+ base: 2.5e-05
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+ qwen_vl_interface: 1.0e-05
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+ logging_frequency: 100
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+ lr_scheduler_type: cosine_with_min_lr
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+ max_train_steps: 2000
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+ num_warmup_steps: 5000
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+ optimizer:
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+ betas:
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+ - 0.9
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+ - 0.95
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+ eps: 1.0e-08
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+ weight_decay: 1.0e-08
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+ pretrained_checkpoint: /content/starVLA_r/results/Checkpoints/Qwen2.5-VL-FAST-LIBERO-4in1/checkpoints/steps_30000_pytorch_model.pt
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+ save_interval: 500
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+ scheduler_specific_kwargs:
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+ min_lr: 1.0e-06
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+ use_lora: true
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+ wandb_entity: michellelin9102-usc
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+ wandb_project: starVLA_Libero
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+ {"steps": 500}
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+ {"steps": 2000}