| --- |
| license: apache-2.0 |
| base_model: |
| - Qwen/Qwen2.5-VL-7B-Instruct |
| tags: |
| - robotics |
| - vision-language-action-model |
| - vision-language-model |
| library_name: transformers |
|
|
| |
| repo: InternRobotics/RoboInter-VLM |
| type: "checkpoint-collection" |
| description: "RoboInterVLM flagship checkpoint (Qwen2.5-VL-7B) fine-tuned on RoboInter-VQA." |
| checkpoints: |
| - name: RoboInter-VLM |
| notes: "Flagship Qwen2.5-VL-7B backbone" |
| --- |
| # RoboInter-VLM: Vision-Language Model for RoboInter Manipulation Suite |
|
|
| **This is the flagship model of the RoboInter-VLM series**, based on [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct). It delivers the strongest performance among the Qwen2.5-VL variants and is the **recommended default checkpoint** for general use. |
|
|
| Developed as part of the [RoboInter](https://github.com/InternRobotics/RoboInter) project. The model is fine-tuned on the [RoboInter-VQA](https://huggingface.co/datasets/InternRobotics/RoboInter-VQA) dataset for intermediate representation understanding and generation in robotic manipulation. |
|
|
| ## All Available Checkpoints |
|
|
| | Checkpoint | Base Model | Architecture | Parameters | Description | Link| |
| |---|---|---|---|---|---| |
| | **`RoboInter-VLM` (this repo)** | [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) | Qwen2.5-VL | ~7B | **Flagship model, recommended for best performance** |https://huggingface.co/InternRobotics/RoboInter-VLM| |
| | `RoboInter-VLM_qwenvl25_3b` | [Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) | Qwen2.5-VL | ~3B | Lightweight model, suitable for efficient deployment | https://huggingface.co/InternRobotics/RoboInter-VLM_qwenvl25_3b| |
| | `RoboInter-VLM_llavaov_7B` | [LLaVA-OneVision-Qwen2-7B](https://huggingface.co/lmms-lab/llava-onevision-qwen2-7b-ov) | LLaVA-OneVision| ~7B | LLaVA-OneVision backbone with SigLIP vision encoder |https://huggingface.co/InternRobotics/RoboInter-VLM_llavaov_7B| |
|
|
| All checkpoints are stored in `safetensors` format with `bfloat16` precision. |
|
|
| ## Supported Tasks |
|
|
| These models are jointly trained on general VQA and three categories of our curated VQA tasks: |
|
|
| - **Generation**: Predicting intermediate representations such as trajectory waypoints, gripper bounding boxes, contact points/boxes, object bounding boxes (current & final), etc. |
| - **Understanding**: Multiple-choice visual reasoning about contact states, grasp poses, object grounding, trajectory selection, movement directions, etc. |
| - **Task Planning**: High-level task planning including next-step prediction, action primitive recognition, success determination, etc. |
|
|
| ## Usage |
|
|
| ### Quick Start (This Model) |
|
|
| ```python |
| from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor |
| |
| model_path = "InternRobotics/RoboInter-VLM" |
| model = Qwen2_5_VLForConditionalGeneration.from_pretrained( |
| model_path, torch_dtype="auto", device_map="auto" |
| ) |
| processor = AutoProcessor.from_pretrained(model_path) |
| ``` |
|
|
| For detailed usage and inference examples, please refer to the [RoboInterVLM-QwenVL](https://github.com/InternRobotics/RoboInter/tree/main/RoboInterVLM/RoboInterVLM-QwenVL) codebase. |
|
|
| ### LLaVA-OneVision Checkpoint |
|
|
| For loading and inference with the LLaVA-OneVision checkpoint, please refer to the [RoboInterVLM-LLaVAOV](https://github.com/InternRobotics/RoboInter/tree/main/RoboInterVLM/RoboInterVLM-LLaVAOV) codebase, as it requires custom model classes. |
|
|
| ### Training & Evaluation |
|
|
| For full training and evaluation pipelines, please refer to: |
|
|
| - **Qwen2.5-VL models**: [RoboInterVLM-QwenVL](https://github.com/InternRobotics/RoboInter/tree/main/RoboInterVLM/RoboInterVLM-QwenVL) |
| - **LLaVA-OneVision model**: [RoboInterVLM-LLaVAOV](https://github.com/InternRobotics/RoboInter/tree/main/RoboInterVLM/RoboInterVLM-LLaVAOV) |
| - **VQA Dataset**: [RoboInter-VQA](https://huggingface.co/datasets/InternRobotics/RoboInter-VQA) |
|
|
| ## Related Resources |
|
|
| - **Project**: [RoboInter](https://github.com/InternRobotics/RoboInter) |
| - **Annotation Data**: [RoboInter-Data](https://huggingface.co/datasets/InternRobotics/RoboInter-Data) |
| - **VQA Dataset**: [RoboInter-VQA](https://huggingface.co/datasets/InternRobotics/RoboInter-VQA) |
|
|
|
|
| ## Citation |
|
|
| If you find RoboInter useful in your research, please consider citing: |
|
|
| ```bibtex |
| @article{li2026robointer, |
| title={RoboInter: A Holistic Intermediate Representation Suite Towards Robotic Manipulation}, |
| author={Li, Hao and Wang, Ziqin and Ding, Zi-han and Yang, Shuai and Chen, Yilun and Tian, Yang and Hu, Xiaolin and Wang, Tai and Lin, Dahua and Zhao, Feng and Liu, Si and Pang, Jiangmiao}, |
| journal={arXiv preprint arXiv:2602.09973}, |
| year={2025} |
| } |
| ``` |
|
|
| ## License |
|
|
| Please refer to the original licenses of [RoboInter](https://github.com/InternRobotics/RoboInter), [Qwen2.5-VL](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct), and [LLaVA-OneVision](https://huggingface.co/lmms-lab/llava-onevision-qwen2-7b-ov). |
|
|
