license: apache-2.0
base_model: Qwen/Qwen3.5-4B
tags:
- robotics
- vision-language-action
- mr0
- pytorch
- mmdit
Meituan-Robotics-0
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 · Getting started · File checksums
| 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 or later, following the Getting Started guide. This loader reads the complete VLA checkpoint without a separate Qwen weight file. Then download this repository:
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 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:
sha256sum -c SHA256SUMS