File size: 3,472 Bytes
3352931 | 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 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | run_id: starvla_qwen3oft_fourier_gr1_unified_1000_franka_epfrac_1_3
run_root_dir: ./playground/Checkpoints
seed: 42
wandb_entity: zwanggk
wandb_project: sq_robocasa
is_debug: false
version_id: '0.21'
framework:
name: QwenOFT
qwenvl:
base_vlm: /gpfs/wangzixuan/shared_project/sq/5-1/starVLA/playground/Pretrained_models/Qwen3-VL-4B-Instruct-Action
attn_implementation: flash_attention_2
vl_hidden_dim: 2048
action_model:
action_model_type: DiT-B
action_dim: 29
action_hidden_dim: 2560
future_action_window_size: 15
past_action_window_size: 0
hidden_size: 2560
add_pos_embed: true
max_seq_len: 1024
state_dim: 58
action_horizon: 16
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:
dropout: 0.2
final_dropout: true
interleave_self_attention: true
norm_type: ada_norm
num_layers: 16
positional_embeddings: null
output_dim: 2560
cross_attention_dim: 2048
dino:
dino_backbone: dinov2_vits14
datasets:
vlm_data:
dataset_py: vlm_datasets
dataformat: llava_json
dataset_use: 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: 4
vla_data:
dataset_py: lerobot_datasets
include_state: false
data_root_dir: /gpfs/wangzixuan/visual_prompting/starVLA_robocasa/playground/Datasets/nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim
data_mix: fourier_gr1_unified_1000
action_type: delta_ee
sequential_step_sampling: false
CoT_prompt: Your task is {instruction}. To identify the key objects for your task.
Locate their bounding boxes in [x1,y1,x2,y2] format.
per_device_batch_size: 32
load_all_data_for_training: true
obs_image_size:
- 224
- 224
delete_pause_frame: false
episode_subset_fraction: 0.3333333333
episode_subset_seed: 42
trainer:
max_train_steps: 100000
num_warmup_steps: 5000
save_interval: 10000
eval_interval: 100
learning_rate:
base: 3.0e-05
qwen_vl_interface: 1.0e-05
action_model: 0.0001
lr_scheduler_type: cosine_with_min_lr
scheduler_specific_kwargs:
min_lr: 5.0e-07
freeze_modules: true
loss_scale:
vla: 1.0
vlm: 0.1
max_grad_norm: 1.0
weight_decay: 0.0
logging_frequency: 10
gradient_clipping: 1.0
gradient_accumulation_steps: 1
optimizer:
name: AdamW
betas:
- 0.9
- 0.95
eps: 1.0e-08
weight_decay: 1.0e-08
reload_modules: qwen_vl_interface
pretrained_checkpoint: /gpfs/wangzixuan/shared_project/sq/5-1/starVLA/playground/Pretrained_models/0426_droid_pretrain_qwen3GR00T_triple_oft_pi_50k_16BS/final_model/pytorch_model.pt
config_yaml: ./examples/Robocasa_tabletop/train_files/starvla_cotrain_robocasa_gr1.yaml
output_dir: ./playground/Checkpoints/starvla_qwen3oft_fourier_gr1_unified_1000_franka_epfrac_1_3
|