| datasets: |
| vla_data: |
| CoT_prompt: Your task is {instruction}. To identify the key objects for your task. |
| Locate their bounding boxes in [x1,y1,x2,y2] format. |
| data_mix: libero_90_task_2 |
| data_root_dir: playground/Datasets/LEROBOT_LIBERO_DATA |
| dataset_py: lerobot_datasets |
| per_device_batch_size: 1 |
| sequential_step_sampling: false |
| video_backend: torchvision_av |
| framework: |
| action_model: |
| action_dim: 7 |
| future_action_window_size: 7 |
| past_action_window_size: 0 |
| name: QwenFast |
| qwenvl: |
| base_vlm: playground/Pretrained_models/Qwen2.5-VL-3B-Instruct-Action |
| output_dir: ./results/Checkpoints/finetune_task2_2000step |
| run_id: finetune_task2_2000step |
| run_root_dir: ./results/Checkpoints |
| seed: 42 |
| trainer: |
| eval_interval: 100 |
| freeze_modules: qwen_vl_interface.model.model.visual,dino_encoder |
| gradient_accumulation_steps: 1 |
| gradient_clipping: 1.0 |
| is_resume: true |
| learning_rate: |
| action_model: 0.0001 |
| base: 2.5e-05 |
| qwen_vl_interface: 1.0e-05 |
| logging_frequency: 100 |
| lr_scheduler_type: cosine_with_min_lr |
| max_train_steps: 2000 |
| num_warmup_steps: 5000 |
| optimizer: |
| betas: |
| - 0.9 |
| - 0.95 |
| eps: 1.0e-08 |
| weight_decay: 1.0e-08 |
| pretrained_checkpoint: /content/starVLA_r/results/Checkpoints/Qwen2.5-VL-FAST-LIBERO-4in1/checkpoints/steps_30000_pytorch_model.pt |
| save_interval: 500 |
| scheduler_specific_kwargs: |
| min_lr: 1.0e-06 |
| wandb_entity: michellelin9102-usc |
| wandb_project: starVLA_Libero |
|
|
