File size: 1,818 Bytes
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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_all
data_root_dir: ./playground/Datasets/LEROBOT_LIBERO_DATA
dataset_py: lerobot_datasets
per_device_batch_size: 8
video_backend: torchvision_av
framework:
action_model:
action_dim: 7
action_horizon: 8
action_model_type: DiT-B
add_pos_embed: true
diffusion_model_cfg:
cross_attention_dim: 4096
dropout: 0.2
final_dropout: true
interleave_self_attention: true
norm_type: ada_norm
num_layers: 16
output_dim: 1024
positional_embeddings: null
future_action_window_size: 7
hidden_size: 1024
max_seq_len: 1024
noise_beta_alpha: 1.5
noise_beta_beta: 1.0
noise_s: 0.999
num_inference_timesteps: 4
num_target_vision_tokens: 32
num_timestep_buckets: 1000
past_action_window_size: 0
state_dim: 7
name: QwenGR00T
qwenvl:
base_vlm: /mnt/18T/starVLAproject/Qwen3-VL-8B-Instruct
output_dir: /starvla/Checkpoints/libero4in1_QwenGR00T_2node_0201_1721
run_id: libero4in1_QwenGR00T_2node_0201_1721
run_root_dir: /starvla/Checkpoints
seed: 42
trainer:
eval_interval: 100
freeze_modules: true
gradient_accumulation_steps: 4
gradient_clipping: 1.0
is_resume: false
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: 80000
num_warmup_steps: 5000
optimizer:
betas:
- 0.9
- 0.95
eps: 1.0e-08
weight_decay: 1.0e-08
save_interval: 10000
scheduler_specific_kwargs:
min_lr: 1.0e-06
wandb_entity: xiguapi
wandb_project: Qwen3VL_libero_all_QwenGR00T_2node
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