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---
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.