Create config.yaml
Browse files- config.yaml +90 -0
config.yaml
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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: all_dataset
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data_root_dir: /mnt/project
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dataset_py: lerobot_datasets
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delete_pause_frame: false
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image_size:
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- 224
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- 224
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lerobot_version: v3.0
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per_device_batch_size: 48
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training_task_weights:
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- 1
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- 1
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- 1
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- 1
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use_delta_action: true
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framework:
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action_model:
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action_dim: 138
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action_horizon: 16
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action_model_type: DiT-L
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add_pos_embed: true
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diffusion_model_cfg:
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cross_attention_dim: 2560
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dropout: 0.2
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final_dropout: true
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interleave_self_attention: true
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norm_type: ada_norm
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num_layers: 16
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output_dim: 2560
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positional_embeddings: null
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future_action_window_size: 15
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future_obs_index: 5
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hidden_size: 2560
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max_num_embodiments: 32
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max_seq_len: 1024
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noise_beta_alpha: 1.5
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noise_beta_beta: 1.0
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noise_s: 0.999
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num_inference_timesteps: 4
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num_target_vision_tokens: 32
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num_timestep_buckets: 1000
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num_views: 1
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obs_horizon: 2
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obs_loss_weight: 1.0
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only_policy: false
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only_wo_video_gen: false
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past_action_window_size: 0
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policy_and_video_gen: false
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state_dim: null
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vision_encoder_path: pretrained
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vision_encoder_size: s
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vision_encoder_type: dinov3
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name: QwenMMDiT
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qwenvl:
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base_vlm: pretrained/vlm/Qwen3-VL-4B-Instruct
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output_dir: checkpoints/lda/pretrain
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run_id: lda-pretrain
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run_root_dir: checkpoints/lda
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seed: 42
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trainer:
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eval_interval: 1000
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freeze_modules: qwen_vl_interface,action_model.vision_encoder
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gradient_accumulation_steps: 1
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gradient_clipping: 1.0
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is_resume: false
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learning_rate:
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action_model: 0.0001
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base: 4.0e-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: 400000
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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: null
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repeated_diffusion_steps: 1
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save_interval: 10000
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scheduler_specific_kwargs:
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min_lr: 5.0e-07
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wandb_entity: Personal
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wandb_project: lda
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