Instructions to use lilkm/vf_stackblocks_iter1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use lilkm/vf_stackblocks_iter1 with LeRobot:
- Notebooks
- Google Colab
- Kaggle
metadata
datasets: lilkm/stackblocks_recap_iter1_demo_rollout_correction
library_name: lerobot
license: apache-2.0
model_name: distributional_value_function
pipeline_tag: robotics
tags:
- lerobot
- reward-model
- distributional_value_function
- robotics
Reward Model Card for distributional_value_function
Reward model type not recognized — please update this template.
This reward model has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs.
How to Get Started with the Reward Model
Train from scratch
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--reward_model.type=distributional_value_function \
--output_dir=outputs/train/<desired_reward_model_repo_id> \
--job_name=lerobot_reward_training \
--reward_model.device=cuda \
--reward_model.repo_id=${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
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