Agents IA
openpi
Physical Intelligence's open vision-language-action models (pi0, pi0-FAST, pi0.5) for robot policies.
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Voir le guide →openvla est un projet de agents IA open source, créé par openvla (openvla/openvla sur GitHub). Il compte 7 123 étoiles sur GitHub et 854 forks, il est publié sous la licence MIT et a été mis à jour pour la dernière fois sur GitHub le 23 mars 2025.
Open-source 7B vision-language-action model and codebase for generalist robot manipulation.
Inference and LoRA fine-tuning need a capable NVIDIA GPU; the README's LoRA example uses a single A100 80GB, and smaller GPUs down to roughly 27GB can work with reduced batch sizes.
For inference, install the minimal requirements and load the checkpoint through Hugging Face transformers; for training, create the conda environment and install the repo editable.
Load openvla/openvla-7b with the AutoProcessor, pass a camera image and an instruction prompt, and read out an action with predict_action.
Download a BridgeData V2 or other Open X-Embodiment dataset in RLDS format (or convert your own), then run the LoRA fine-tuning script - or the newer OFT recipe the project now recommends.
openvla-7b is trained on a mixture of Open X-Embodiment datasets spanning 970K trajectories, with checkpoints, configs and model cards on Hugging Face.
A lightweight transformers interface (AutoProcessor and AutoModelForVision2Seq) loads the model with few dependencies, and a REST API serving script is included for robot stacks without on-device compute.
Built-in examples cover full, partial and (quantized) LoRA fine-tuning via Hugging Face PEFT, plus full fine-tuning with PyTorch FSDP for multi-GPU nodes.
PyTorch FSDP and Flash-Attention power training across model sizes, with native support for arbitrary RLDS dataset mixtures.
The project documents the FAST tokenizer, which compresses action chunks into fewer tokens for much faster inference with discrete actions.
The 2025 OFT update offers an optimized fine-tuning recipe the team recommends for faster inference, higher success rates, multiple input images and high-frequency bimanual control.
Clone the OpenVLA codebase for the training, fine-tuning and serving scripts.
git clone https://github.com/openvla/openvla.git
cd openvlaSet up the pinned Python 3.10 / PyTorch 2.2 environment from the README, then install the package editable.
conda create -n openvla python=3.10 -y
conda activate openvla
pip install -e .Install the minimal requirements and load openvla-7b in a few lines of Python.
pip install -r https://raw.githubusercontent.com/openvla/openvla/main/requirements-min.txt
python -c "from transformers import AutoProcessor; AutoProcessor.from_pretrained('openvla/openvla-7b', trust_remote_code=True)" # see README for the full inference exampleThe OpenVLA project team maintains the repository; it is built on the Prismatic VLMs codebase and released alongside the OpenVLA research paper.
The code is MIT-licensed. The pretrained checkpoints are derived from Llama-2, so their use is governed by the Llama Community License rather than MIT - check its terms before commercial deployment.
No. Models run locally from Hugging Face checkpoints; you need your own GPU, not any hosted API.
Both are open VLA projects: OpenVLA centers on a single 7B autoregressive model with public training code and LoRA/OFT recipes, while openpi focuses on Physical Intelligence's pi-series flow-matching models.
Install the minimal requirements, load openvla/openvla-7b with transformers, and run the README's inference example with a sample image and instruction.
Physical Intelligence's open vision-language-action models (pi0, pi0-FAST, pi0.5) for robot policies.
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