Instructions to use wzn12/critic_warmup_smolvla_ring with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use wzn12/critic_warmup_smolvla_ring with LeRobot:
# See https://github.com/huggingface/lerobot?tab=readme-ov-file#installation for more details git clone https://github.com/huggingface/lerobot.git cd lerobot pip install -e .[smolvla]
# Launch finetuning on your dataset python lerobot/scripts/train.py \ --policy.path=wzn12/critic_warmup_smolvla_ring \ --dataset.repo_id=lerobot/svla_so101_pickplace \ --batch_size=64 \ --steps=20000 \ --output_dir=outputs/train/my_smolvla \ --job_name=my_smolvla_training \ --policy.device=cuda \ --wandb.enable=true
# Run the policy using the record function python -m lerobot.record \ --robot.type=so101_follower \ --robot.port=/dev/ttyACM0 \ # <- Use your port --robot.id=my_blue_follower_arm \ # <- Use your robot id --robot.cameras="{ front: {type: opencv, index_or_path: 8, width: 640, height: 480, fps: 30}}" \ # <- Use your cameras --dataset.single_task="Grasp a lego block and put it in the bin." \ # <- Use the same task description you used in your dataset recording --dataset.repo_id=HF_USER/dataset_name \ # <- This will be the dataset name on HF Hub --dataset.episode_time_s=50 \ --dataset.num_episodes=10 \ --policy.path=wzn12/critic_warmup_smolvla_ring - Notebooks
- Google Colab
- Kaggle
Critic Warmup Model for SmolVLA-SAC
这是一个为 SmolVLA-SAC 策略训练的 Critic 模型预热权重。
训练配置
- 数据集: wzn12/teleop_ring_labeled
- 训练轮数: 20
- 批大小: 48
- 学习率: 0.0003
使用方法
from lerobot.policies.smolvla_sac.modeling_smolvla_sac import SmolVLASACPolicy
import torch
# 加载模型
checkpoint = torch.load("final_critic.pth")
critic_state_dict = checkpoint['critic_state_dict']
# 在你的 SmolVLASACPolicy 中加载权重
policy.critic.load_state_dict(critic_state_dict)
训练于: 2025-08-14 17:42:03
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