---
license: apache-2.0
base_model: Qwen/Qwen3.5-4B
tags:
- robotics
- vision-language-action
- mr0
- pytorch
- mmdit
---

# Meituan-Robotics-0

[Technical Report](https://arxiv.org/abs/2611.08577)

MR0 continuous-action pretraining model with a Qwen3.5-4B vision-language backbone and an MMDiT action head. Adapt this pretrained VLA to downstream robot embodiments and tasks.

[Code](https://github.com/meituan/Meituan-Robotics-0) · [Getting started](https://github.com/meituan/Meituan-Robotics-0/blob/main/GETTING_STARTED.md) · [File checksums](SHA256SUMS)

| Property | Value |
|---|---|
| Framework | QwenMMDiT |
| Action dimensions | 20 |
| Action horizon | 16 |
| Weights | `checkpoints/model.pt` |
| Configuration | `config.full.yaml` |
| Normalization statistics | `dataset_statistics.json` |

## Download and load

Install the MR0 code at commit [`88c66793`](https://github.com/meituan/Meituan-Robotics-0/commit/88c667933dfc941a71c9b142eb84b716ac4f0707) or later, following the Getting Started guide. This loader reads the complete VLA checkpoint without a separate Qwen weight file. Then download this repository:

```python
import os
from pathlib import Path
from huggingface_hub import snapshot_download

root = Path(snapshot_download(
    repo_id="MeituanRobotics/Meituan-Robotics-0",
    local_dir="Meituan-Robotics-0",
)).resolve()
os.environ["MR0_RELEASE_DIR"] = str(root)

from mr0.model.framework.base_framework import baseframework

model = baseframework.from_pretrained(root / "checkpoints/model.pt")
model = model.eval().to("cuda")
```

`checkpoints/model.pt` contains the complete Qwen3.5 backbone and MMDiT action head. `base_vlm/Qwen3.5-4B-Action/` contains only the matching tokenizer, processor, and architecture configuration; it is not a second model. The release also includes the VLA configuration and normalization statistics. Choose the normalization key in `dataset_statistics.json` that matches your robot embodiment.

The checkpoint bytes are unchanged. `release.json` records the model provenance and `SHA256SUMS` records file identities. For downstream training, adapt the dataset settings and set both `framework.qwenvl.checkpoint_path` and `trainer.pretrained_checkpoint` to the downloaded complete checkpoint. Set `framework.qwenvl.base_vlm` to its metadata directory.

## File verification

[SHA256SUMS](SHA256SUMS) is the single SHA-256 checksum manifest for this release. It covers every repository file except the manifest itself, including weights, configuration, tokenizer/processor metadata, and documentation.

After downloading the complete repository, run from its root directory:

```bash
sha256sum -c SHA256SUMS
```

## Acknowledgements

We thank [Qwen](https://github.com/QwenLM), [StarVLA](https://github.com/starVLA/starVLA), and the [RoboTwin](https://github.com/RoboTwin-Platform/RoboTwin) community.