| datasets: wuc1/bi_so101_ffp_0630 | |
| library_name: lerobot | |
| license: apache-2.0 | |
| model_name: sarm | |
| pipeline_tag: robotics | |
| tags: | |
| - sarm | |
| - reward-model | |
| - robotics | |
| - lerobot | |
| # Reward Model Card for sarm | |
| <!-- Provide a quick summary of what the reward model is/does. --> | |
| A Success-Aware Reward Model (SARM) predicts a dense reward signal from observations, typically used downstream for reinforcement learning or human-in-the-loop fine-tuning when task success is not directly observable. | |
| This reward model has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). | |
| See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). | |
| --- | |
| ## How to Get Started with the Reward Model | |
| ### Train from scratch | |
| ```bash | |
| lerobot-train \ | |
| --dataset.repo_id=${HF_USER}/<dataset> \ | |
| --reward_model.type=sarm \ | |
| --output_dir=outputs/train/<desired_reward_model_repo_id> \ | |
| --job_name=lerobot_reward_training \ | |
| --reward_model.device=cuda \ | |
| --reward_model.repo_id=${HF_USER}/<desired_reward_model_repo_id> \ | |
| --wandb.enable=true | |
| ``` | |
| _Writes checkpoints to `outputs/train/<desired_reward_model_repo_id>/checkpoints/`._ | |
| ### Load the reward model in Python | |
| ```python | |
| from lerobot.rewards import make_reward_model | |
| reward_model = make_reward_model(pretrained_path="<hf_user>/<reward_model_repo_id>") | |
| reward = reward_model.compute_reward(batch) | |
| ``` | |
| --- | |
| ## Model Details | |
| - **License:** apache-2.0 |