Instructions to use lilkm/vf_stackblocks_temporal_siglip_hl_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use lilkm/vf_stackblocks_temporal_siglip_hl_v1 with LeRobot:
- Notebooks
- Google Colab
- Kaggle
metadata
datasets: lilkm/stackblocks_recap_all_for_vf_v3
library_name: lerobot
license: apache-2.0
model_name: temporal_siglip_value_function
pipeline_tag: robotics
tags:
- reward-model
- robotics
- lerobot
- temporal_siglip_value_function
Reward Model Card for temporal_siglip_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=${HF_USER}/<dataset> \
--reward_model.type=temporal_siglip_value_function \
--output_dir=outputs/train/<desired_reward_model_repo_id> \
--job_name=lerobot_reward_training \
--reward_model.device=cuda \
--reward_model.repo_id=${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