File size: 1,495 Bytes
1b90f1c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | datasets:
vla_data:
data_mix: robotwin
data_root_dir: /inspire/ssd/project/embodied-basic-model/zhangjianing-253108140206/DATASET/robotwin_lerobot
dataset_py: lerobot_datasets
image_size:
- 448
- 448
per_device_batch_size: 8
video_backend: torchvision_av
framework:
action_model:
action_dim: 14
action_hidden_dim: 2560
action_model_type: DiT-B
future_action_window_size: 15
past_action_window_size: 0
name: QwenOFT
qwenvl:
base_vlm: /inspire/qb-ilm/project/embodied-basic-model/zhangjianing-253108140206/model/cubev0-200000-Qwen3-VL
output_dir: /inspire/qb-ilm/project/embodied-basic-model/zhangjianing-253108140206/checkpoints/cubev0-robotwin-finetune-oft/cubev0_robotwin_200000_groot
run_id: cubev0_robotwin_200000_groot
run_root_dir: /inspire/qb-ilm/project/embodied-basic-model/zhangjianing-253108140206/checkpoints/cubev0-robotwin-finetune-oft
seed: 42
trainer:
eval_interval: 1000
freeze_modules: true
gradient_accumulation_steps: 1
gradient_clipping: 1.0
is_resume: false
learning_rate:
action_model: 0.0001
base: 1.0e-05
qwen_vl_interface: 1.0e-05
logging_frequency: 50
lr_scheduler_type: cosine_with_min_lr
max_train_steps: 30000
num_warmup_steps: 100
optimizer:
betas:
- 0.9
- 0.95
eps: 1.0e-08
weight_decay: 1.0e-08
save_interval: 5000
scheduler_specific_kwargs:
min_lr: 5.0e-07
wandb_entity: zaleni-tongji-university
wandb_project: cubev0-robotwin-finetune
|