{
  "run_id": "1008_qwenLfm_briage",
  "run_root_dir": "./results/Checkpoints",
  "seed": 42,
  "trackers": [
    "jsonl",
    "wandb"
  ],
  "wandb_entity": "jinhuiye",
  "wandb_project": "InternM1",
  "is_debug": false,
  "framework": {
    "name": "QwenGR00T",
    "qwenvl": {
      "base_vlm": "./playground/Pretrained_models/Qwen2.5-VL-3B-Instruct",
      "attn_implementation": "flash_attention_2",
      "vl_hidden_dim": 2048
    },
    "dino": {
      "dino_backbone": "dinov2_vits14"
    },
    "action_model": {
      "action_model_type": "DiT-L",
      "hidden_size": 1024,
      "add_pos_embed": true,
      "max_seq_len": 1024,
      "action_dim": 7,
      "state_dim": 7,
      "future_action_window_size": 15,
      "action_horizon": 16,
      "past_action_window_size": 0,
      "repeated_diffusion_steps": 8,
      "noise_beta_alpha": 1.5,
      "noise_beta_beta": 1.0,
      "noise_s": 0.999,
      "num_timestep_buckets": 1000,
      "num_inference_timesteps": 4,
      "num_target_vision_tokens": 32,
      "diffusion_model_cfg": {
        "cross_attention_dim": 2048,
        "dropout": 0.2,
        "final_dropout": true,
        "interleave_self_attention": true,
        "norm_type": "ada_norm",
        "num_layers": 16,
        "output_dim": 1024,
        "positional_embeddings": null
      },
      "action_hidden_dim": 2048
    }
  },
  "datasets": {
    "vlm_data": {
      "dataset_py": "vlm_datasets",
      "dataformat": "llava_json",
      "dataset_use": "aokvqa_cauldron_llava_format,sharegpt4v_coco,sharegpt4v_knowledge,sharegpt4v_llava,sharegpt4v_sam,asv2_conversation_en,asv2_detailed_description_en,asv2_region_captioning_en,coco_internvl_longcap_en,coco_karpathy_train_567_en,coco_negative_gpt4o_en,coco_poetry_zh,coco_rem_en_zh,cocorem_exist_yorn_en,cocotextv2_en,cocotextv2_gpt4o_en,okvqa_en,refcoco_grounding_aug_en,refcoco_grounding_en,tallyqa_coco_en,toloka_grounding_aug_en,vqav2_en,vsr_en",
      "eval_dataset": "aokvqa_cauldron_llava_format",
      "data_flatten": false,
      "base_interval": 2,
      "max_pixels": 50176,
      "min_pixels": 784,
      "model_max_length": 2048,
      "model_type": "qwen2.5vl",
      "per_device_batch_size": 3
    },
    "vla_data": {
      "dataset_py": "lerobot_datasets",
      "data_root_dir": "playground/Datasets/OXE_LEROBOT",
      "data_mix": "bridge",
      "action_type": "delta_ee",
      "CoT_prompt": "Your task is {instruction}. To identify the key objects for your task. Locate their bounding boxes in [x1,y1,x2,y2] format.",
      "CoT_answer": "bbox",
      "default_image_resolution": [
        3,
        224,
        224
      ],
      "per_device_batch_size": 16,
      "load_all_data_for_training": true,
      "obs": [
        "image_0"
      ],
      "image_size": [
        224,
        224
      ]
    }
  },
  "trainer": {
    "epochs": 100,
    "max_train_steps": 100000,
    "num_warmup_steps": 10000,
    "save_interval": 5000,
    "eval_interval": 1000,
    "learning_rate": {
      "base": 3e-05,
      "qwen_vl_interface": 1e-05,
      "action_model": 0.0001
    },
    "lr_scheduler_type": "cosine_with_min_lr",
    "scheduler_specific_kwargs": {
      "min_lr": 5e-07
    },
    "freeze_modules": true,
    "loss_scale": {
      "vla": 1.0,
      "vlm": 0.1
    },
    "repeated_diffusion_steps": 4,
    "max_grad_norm": 1.0,
    "warmup_ratio": 0.1,
    "weight_decay": 0.0,
    "logging_frequency": 10,
    "gradient_clipping": 1.0,
    "gradient_accumulation_steps": 1,
    "optimizer": {
      "name": "AdamW",
      "betas": [
        0.9,
        0.95
      ],
      "eps": 1e-08,
      "weight_decay": 1e-08
    },
    "is_resume": false,
    "resume_epoch": null,
    "resume_step": null,
    "enable_gradient_checkpointing": true,
    "enable_mixed_precision_training": true
  },
  "is_resume": false,
  "output_dir": "./results/Checkpoints/1008_qwenLfm_briage"
}