Instructions to use agents2agents/MolmoAct2-LIBERO-Jetson-Thor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TensorRT
How to use agents2agents/MolmoAct2-LIBERO-Jetson-Thor with TensorRT:
# 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
|
Download README.md from agents2agents/MolmoAct2-LIBERO-Jetson-Thor: direct link, hf CLI and curl.
- Browser
- Download file 2.04 kB
-
https://huggingface.co/agents2agents/MolmoAct2-LIBERO-Jetson-Thor/resolve/main/README.md
- Command line
-
hf download hf://agents2agents/MolmoAct2-LIBERO-Jetson-Thor/README.md
-
curl -L -o README.md https://huggingface.co/agents2agents/MolmoAct2-LIBERO-Jetson-Thor/resolve/main/README.md
2.04 kB
| license: apache-2.0 | |
| base_model: allenai/MolmoAct2-LIBERO | |
| tags: | |
| - robotics | |
| - vision-language-action | |
| - tensorrt | |
| - jetson | |
| - molmoact2 | |
| - libero | |
| - edge-inference | |
| # MolmoAct2 LIBERO engines for Jetson AGX Thor | |
| Prebuilt TensorRT engines for running | |
| [`allenai/MolmoAct2-LIBERO`](https://huggingface.co/allenai/MolmoAct2-LIBERO) | |
| with [vla-edge](https://github.com/Agents2AgentsAI/vla-edge). This is the | |
| configuration used for our 2,000-episode LIBERO evaluation. | |
| With the FP8 vision engine enabled, it scored **97.2% overall**, matching the | |
| MolmoAct2 paper result within sampling noise. See the | |
| [performance writeup](https://agents2agents.ai/blog/molmoact2-jetson-thor) for | |
| the methodology and per-suite results. | |
| ## Download and verify | |
| This repository is about 10.5 GB. | |
| ```bash | |
| hf download agents2agents/MolmoAct2-LIBERO-Jetson-Thor \ | |
| --local-dir vla-edge-libero | |
| cd vla-edge-libero | |
| python -c "from vla_edge.backends.tensorrt import artifacts; \ | |
| artifacts.check_compatible('.'); artifacts.verify_checksums('.'); \ | |
| print('bundle verified')" | |
| ``` | |
| ## Run | |
| ```bash | |
| vla-edge-serve --embodiment libero --backend tensorrt \ | |
| --engine-dir /path/to/vla-edge-libero/libero --fast-vision | |
| ``` | |
| The public API expects two images named `image` and `wrist_image`, plus the | |
| eight-value LIBERO state vector. The startup warmup executes every compiled | |
| stage. | |
| ## Hardware and contents | |
| These plans require an NVIDIA Jetson AGX Thor Developer Kit with JetPack R39 | |
| rev 2.1 and TensorRT 10.16.2.10. | |
| ```text | |
| libero/ fixed 704-token engines and FP8 vision option | |
| host/libero/ processor, normalization, embeddings, and flow weights | |
| MANIFEST.json compatibility requirements and checksums | |
| ``` | |
| Serving this bundle is local and does not download the upstream checkpoint. | |
| ## License | |
| Apache-2.0. See [LICENSE](LICENSE) and [NOTICE](NOTICE). | |
| The plans embed weights from `allenai/MolmoAct2-LIBERO`, released by the Allen | |
| Institute for AI under Apache-2.0. The conversion changes execution, not the | |
| checkpoint parameters. | |