Robotics
Transformers
Safetensors
qwen2_5_vl
image-text-to-text
vision-language-action-model
vision-language-model
text-generation-inference
Instructions to use InternRobotics/InternVLA-M1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InternRobotics/InternVLA-M1 with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("InternRobotics/InternVLA-M1") model = AutoModelForMultimodalLM.from_pretrained("InternRobotics/InternVLA-M1") - Notebooks
- Google Colab
- Kaggle
metadata
license: cc-by-nc-sa-4.0
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
tags:
- robotics
- vision-language-action-model
- vision-language-model
library_name: transformers
Model Card for InternVLA-M1
Description:
InternVLA-M1 is an open-source, end-to-end vision–language–action (VLA) framework for building and researching generalist robot policies. The checkpoints in this repository were pretrained on the system2 dataset.
- 🌐 Homepage: InternVLA-M1 Project Page
- 💻 Codebase: InternVLA-M1 GitHub Repo
Citation
@misc{internvla2024,
title = {InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy},
author = {InternVLA-M1 Contributors},
year = {2025},
booktitle={arXiv},
}
