Instructions to use hesh0629/groot_probe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use hesh0629/groot_probe with LeRobot:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
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Download README.md from hesh0629/groot_probe: direct link, hf CLI and curl.
- Browser
- Download file 1.49 kB
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https://huggingface.co/hesh0629/groot_probe/resolve/main/README.md
- Command line
-
hf download hf://hesh0629/groot_probe/README.md
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curl -L -o README.md https://huggingface.co/hesh0629/groot_probe/resolve/main/README.md
1.49 kB
metadata
datasets: hesh0629/put_banana_v2
library_name: lerobot
license: apache-2.0
model_name: groot
pipeline_tag: robotics
tags:
- groot
- robotics
- lerobot
Model Card for groot
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