Embodied.cpp GGUF Models

This repository provides GGUF model weights converted for use with Embodied.cpp, a portable C++ inference runtime for embodied AI models on heterogeneous robots.

These files are deployment-oriented conversions of upstream checkpoints. They are intended for inference with Embodied.cpp, rather than for training or fine-tuning in the original Python stacks.

About Embodied.cpp

Embodied.cpp is a portable inference runtime for embodied AI models, including Vision-Language-Action (VLA) models and World-Action Models (WAMs). It focuses on low-latency closed-loop deployment across heterogeneous hardware and robot systems.

Paper:

• Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots
• arXiv: https://arxiv.org/abs/2607.02501

What This Repository Contains

This repository hosts GGUF-converted model artifacts used by embodied.cpp.

The goal is to make upstream embodied model checkpoints easier to deploy from C++ with a unified runtime and serving interface.

Conversion Provenance

The GGUF files in this repository are converted from upstream checkpoints using the conversion scripts in the embodied.cpp project.

1. pi0.5

Upstream checkpoint:

• https://huggingface.co/lerobot/pi05_libero_finetuned_v044

Conversion scripts:

• scripts/convert_pi05_to_gguf.py
• scripts/convert_pi05_mmproj_to_gguf.py

Notes:

• The converted artifacts are intended for embodied.cpp pi0.5 inference.
• pi0.5 uses separate GGUF exports for the main policy weights and the multimodal projector / vision-side component.

2. Hunyuan-VLA-0.5

Upstream checkpoint:

• https://huggingface.co/tencent/Hy-Embodied-0.5-VLA-RoboTwin

Conversion code:

• Conversion scripts are included in the embodied.cpp repository.

Notes:

• These GGUF files target embodied.cpp deployment of HY-VLA on RoboTwin-style evaluation and related runtime paths.

3. LingBot-VA

Upstream source:

• https://huggingface.co/datasets/robbyant/libero-long-lerobot

Conversion code:

• Conversion scripts are included in the embodied.cpp repository.

Notes:

• These GGUF artifacts are prepared for the LingBot-VA path in embodied.cpp.

Intended Use

These GGUF files are intended for:

• Running embodied AI models with embodied.cpp
• C++ inference and server deployment
• Simulation and robotics evaluation pipelines supported by embodied.cpp

These files are not the original training checkpoints and may not be directly compatible with upstream Python inference code without the embodied.cpp runtime.

Usage

Please use these weights with the corresponding embodied.cpp model server or evaluation pipeline.

Typical usage includes:

• vla-pi05-server
• vla-hy-vla-server
• wam-lingbot-server

Please refer to the embodied.cpp project repository and documentation for build instructions, model-specific runtime requirements, and evaluation examples.

Roadmap

This repository will continue to be updated with GGUF releases for:

• pi0.5
• Hunyuan-VLA-0.5
• LingBot-VA

Additional embodied.cpp-compatible model exports may be added in the future.

Attribution

All model credit belongs to the original upstream authors and maintainers.

Upstream sources referenced here:

• pi0.5 (LeRobot checkpoint): https://huggingface.co/lerobot/pi05_libero_finetuned_v044
• HY-VLA RoboTwin checkpoint: https://huggingface.co/tencent/Hy-Embodied-0.5-VLA-RoboTwin
• LingBot-VA related source dataset: https://huggingface.co/datasets/robbyant/libero-long-lerobot

License and Restrictions

This repository contains converted deployment artifacts derived from upstream resources.

Please review and comply with the original upstream licenses, model terms, and dataset restrictions before use or redistribution.

Because different upstream sources may have different licenses or usage constraints, users are responsible for checking the original source pages.

Citation

If you find these GGUF conversions or embodied.cpp useful, please cite:

@article{xu2026embodiedcpp,
  title={Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots},
  author={Xu, Ling and Han, Chuyu and Li, Borui and Wu, Hao and Jiang, Shiqi and Cao, Ting and Li, Chuanyou and Zhong, Sheng and Wang, Shuai},
  journal={arXiv preprint arXiv:2607.02501},
  year={2026}
}