0b2285d1ec
* feat(policies): Initial setup to push policies to hub with tags and model card * feat: add dataset that is used to train * Add model template summary * fix: Update link model_card template * fix: remove print * fix: change import name * fix: add model summary in template * fix: minor text * fix: comments Lucain * fix: feedback steven * fix: restructure push to hub * fix: remove unneeded changes * fix: import * fix: import 2 * Add MANIFEST.in * fix: feedback pr * Fix tests * tests: Add smolvla end-to-end test * Fix: smolvla test * fix test name * fix policy tests * Add push to hub false policy tests * Do push to hub cleaner * fix(ci): add push_to_hub false in tests --------- Co-authored-by: Steven Palma <steven.palma@huggingface.co>
75 lines
3.9 KiB
Markdown
75 lines
3.9 KiB
Markdown
---
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# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
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# Doc / guide: https://huggingface.co/docs/hub/model-cards
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{{ card_data }}
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---
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# Model Card for {{ model_name | default("Model ID", true) }}
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<!-- Provide a quick summary of what the model is/does. -->
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{% if model_name == "smolvla" %}
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[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
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{% elif model_name == "act" %}
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[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
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{% elif model_name == "tdmpc" %}
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[TD-MPC](https://huggingface.co/papers/2203.04955) combines model-free and model-based approaches to improve sample efficiency and performance in continuous control tasks by using a learned latent dynamics model and terminal value function.
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{% elif model_name == "diffusion" %}
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[Diffusion Policy](https://huggingface.co/papers/2303.04137) treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation.
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{% elif model_name == "vqbet" %}
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[VQ-BET](https://huggingface.co/papers/2403.03181) combines vector-quantised action tokens with Behaviour Transformers to discretise control and achieve data-efficient imitation across diverse skills.
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{% elif model_name == "pi0" %}
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[Pi0](https://huggingface.co/papers/2410.24164) is a generalist vision-language-action transformer that converts multimodal observations and text instructions into robot actions for zero-shot task transfer.
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{% elif model_name == "pi0fast" %}
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[Pi0-Fast](https://huggingface.co/papers/2501.09747) is a variant of Pi0 that uses a new tokenization method called FAST, which enables training of an autoregressive vision-language-action policy for high-frequency robotic tasks with improved performance and reduced training time.
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{% elif model_name == "sac" %}
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[Soft Actor-Critic (SAC)](https://huggingface.co/papers/1801.01290) is an entropy-regularised actor-critic algorithm offering stable, sample-efficient learning in continuous-control environments.
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{% elif model_name == "reward_classifier" %}
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A reward classifier is a lightweight neural network that scores observations or trajectories for task success, providing a learned reward signal or offline evaluation when explicit rewards are unavailable.
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{% else %}
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_Model type not recognized — please update this template._
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{% endif %}
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This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
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See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
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---
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## How to Get Started with the Model
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For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
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Below is the short version on how to train and run inference/eval:
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### Train from scratch
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```bash
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python lerobot/scripts/train.py \
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--dataset.repo_id=${HF_USER}/<dataset> \
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--policy.type=act \
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--output_dir=outputs/train/<desired_policy_repo_id> \
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--job_name=lerobot_training \
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--policy.device=cuda \
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--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
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--wandb.enable=true
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```
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*Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`.*
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### Evaluate the policy/run inference
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```bash
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python -m lerobot.record \
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--robot.type=so100_follower \
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--dataset.repo_id=<hf_user>/eval_<dataset> \
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--policy.path=<hf_user>/<desired_policy_repo_id> \
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--episodes=10
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```
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Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
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---
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## Model Details
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* **License:** {{ license | default("\[More Information Needed]", true) }}
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