Instructions to use globcy/openvla_high_level_dir_only_90_bottom_up with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use globcy/openvla_high_level_dir_only_90_bottom_up with LeRobot:
- Notebooks
- Google Colab
- Kaggle
metadata
datasets: globcy/libero_dir_trace_90
library_name: lerobot
license: apache-2.0
model_name: openvla
pipeline_tag: robotics
tags:
- openvla
- robotics
- lerobot
Model Card for openvla
Model type not recognized — please update this template.
This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs.
How to Get Started with the Model
For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval:
Train from scratch
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
Writes checkpoints to outputs/train/<desired_policy_repo_id>/checkpoints/.
Evaluate the policy/run inference
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
Prefix the dataset repo with eval_ and supply --policy.path pointing to a local or hub checkpoint.
Model Details
- License: apache-2.0