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README.md

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+# RoboInterVLM: Vision-Language Model Checkpoints for RoboInter Manipulation Suite
+
+Model checkpoints of **RoboInterVLM**, developed as part of the [RoboInter](https://github.com/InternRobotics/RoboInter) project. These models are fine-tuned on the [RoboInter-VQA](https://huggingface.co/datasets/InternRobotics/RoboInter-VQA) dataset for intermediate representation understanding and generation in robotic manipulation.
+
+## Available Checkpoints
+
+| Checkpoint | Base Model | Architecture | Parameters | Description |
+|---|---|---|---|---|
+| `RoboInterVLM_qwenvl25_3b` | [Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) | Qwen2.5-VL | ~3B | Lightweight Qwen2.5VL model, suitable for efficient deployment |
+| `RoboInterVLM_qwenvl25_7b` | [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) | Qwen2.5-VL | ~7B | Larger Qwen2.5-VL backbone for stronger performance |
+| `RoboInterVLM_llava_one_vision_7B` | [LLaVA-OneVision-Qwen2-7B](https://huggingface.co/lmms-lab/llava-onevision-qwen2-7b-ov) | LLaVA-OneVision (SigLIP + Qwen2) | ~7B | LLaVA-OneVision backbone with SigLIP vision encoder |
+
+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
+
+### Qwen2.5-VL Checkpoints
+For loading and inference with the Qwen2.5-VL checkpoint, please refer to the [RoboInterVLM-QwenVL](https://github.com/InternRobotics/RoboInter/tree/main/RoboInterVLM/RoboInterVLM-QwenVL) codebase. We provide a fast loading example below:
+
+```python
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
+
+model_path = "InternRobotics/RoboInterVLM_qwenvl25_3b"  # or RoboInterVLM_qwenvl25_7b
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    model_path, torch_dtype="auto", device_map="auto"
+)
+processor = AutoProcessor.from_pretrained(model_path)
+```
+
+### 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)
+## 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).