--- license: apache-2.0 --- # S1-VL-32B: Scientific Multimodal Reasoning Model [δΈ­ζ–‡η‰ˆ](./README_zh.md) | [English](./README.md) ## πŸ”¬ Introduction **S1-VL-32B** is a multimodal large language model for scientific domains, developed by the ScienceOne team at the Chinese Academy of Sciences. It natively supports two reasoning paradigms β€” **Multimodal Reasoning** and **Thinking with Images** β€” and achieves state-of-the-art performance across multiple mainstream scientific multimodal evaluation benchmarks. - **Multimodal Reasoning Mode**: Chain-of-thought-based multimodal scientific reasoning, designed for the analysis and solving of complex, multi-step problems. - **Thinking with Images Mode**: Enables the model to actively invoke code tools during the reasoning process to perform image operations β€” including cropping, zooming, image enhancement, bounding box annotation, and keypoint marking β€” before generating responses. We have established a **cross-disciplinary data processing pipeline** that conducts multi-dimensional utility evaluation and filtering of visual reasoning trajectories to ensure the quality of training data. A **multi-stage post-training procedure** is employed to progressively unlock the scientific reasoning capabilities of S1-VL-32B: - **Stage 1**: Large-scale multimodal instruction data spanning multiple disciplines β€” including **mathematics, physics, chemistry, astronomy, earth sciences, and biology** β€” is used for mixed training to enhance the model's scientific visual understanding and logical reasoning abilities, laying a solid foundation for academic figure Q&A, medical image analysis, chemical structure recognition, and related tasks. - **Stage 2**: The **Thinking with Images** reasoning paradigm is introduced. Through high-quality **scientific reasoning data annealing**, the model acquires the ability to perform **image operations via code** during inference. This approach yields particularly outstanding performance in scenarios requiring fine-grained image analysis, with notable strengths in interpreting dense scientific charts, high-resolution remote sensing imagery, microscopic images, and complex visual scenes such as astronomical observation data. ## πŸ“‚ Model Weights | Model | Parameters | HuggingFace | ModelScope | |-------|-----------|-------------|------------| | S1-VL-32B | 32B | πŸ€— [Download](https://huggingface.co/ScienceOne-AI/S1-VL-32B) | πŸ€– [Download](https://modelscope.cn/models/ScienceOne-AI/S1-VL-32B) | ## πŸ† Evaluation Results The evaluation covers **2 dimensions** and **13 benchmarks**. The **Scientific Multimodal Reasoning** dimension includes MMMU, SFE, MathVision, Physics, ScienceOlympiad, VRSBench-MINI, GMAI-MMBench, and Galaxy-10-DECaLS, spanning mathematics, physics, medicine, remote sensing, astronomy, and other professional fields. The **Image Manipulation Reasoning** dimension includes HRBench-4K, HRBench-8K, MME-RealWorld-CN, MME-RealWorld-Lite, and V*, focusing on high-resolution image understanding and real-world visual reasoning.
S1-VL-32B demonstrates outstanding overall competitiveness across the aforementioned evaluations. In **scientific multimodal reasoning** tasks, the model achieves significant advantages on multiple authoritative benchmarks β€” including MMMU, MathVision, and VRSBench-MINI β€” surpassing its base model Qwen3-VL-32B in overall performance, while remaining highly competitive against open-source models with substantially larger parameter scales (e.g., Qwen3-VL-235B, Intern-S1) as well as closed-source flagship models (e.g., Gemini 2.5 Pro, GPT-5). In **image operation reasoning** tasks, S1-VL-32B ranks **first across all five benchmark evaluations**, comprehensively outperforming models of comparable and larger scales, while also surpassing dedicated "Thinking with Images" models such as Thyme-VL and Skywork-R1V4. These results fully validate its ability to achieve efficient, high-quality multimodal reasoning at the 32B parameter scale. ## 🧠 Case Study The following presents reasoning examples of S1-VL-32B operating in **Thinking with Images** mode. When processing a low-resolution cervical CT image, S1-VL-32B proactively invokes code tools during its reasoning process to perform **cropping and magnification** on the region of interest. By obtaining a clearer local image, the model then combines the enhanced visual information with its internal knowledge to complete the reasoning.
πŸ“ More cases are available in [CASES.md](./CASES.md). ## πŸš€ Quick Start ### 1. Install Dependencies ```bash # Requires vLLM >= 0.11.0 pip install -U vllm pip install qwen-vl-utils==0.0.14 ``` ### 2. Start the vLLM Service ```bash vllm serve ScienceOne-AI/S1-VL-32B \ --tensor-parallel-size 4 \ --max-model-len 32768 \ --limit-mm-per-prompt image=15 \ --reasoning-parser deepseek_r1 \ --enable-prefix-caching \ --gpu-memory-utilization 0.95 \ --port 9200 ``` ### 3. Multimodal Reasoning Mode ```python from openai import OpenAI import base64 client = OpenAI(api_key="EMPTY", base_url="http://localhost:9200/v1") with open("path/to/your/image.png", "rb") as f: image_data = base64.b64encode(f.read()).decode("utf-8") response = client.chat.completions.create( model="ScienceOne-AI/S1-VL-32B", messages=[ { "role": "user", "content": [ {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_data}"}}, {"type": "text", "text": "Please describe the physical phenomenon shown in the image and derive the relevant equations."}, ], } ], temperature=0.6, top_p=0.95, max_tokens=16384, ) # The reasoning process is in the reasoning_content field print("Thinking process:\n", response.choices[0].message.reasoning_content) print("\nFinal answer:\n", response.choices[0].message.content) ``` ### 4. Thinking with Images Mode Thinking with Images mode requires deploying a **code sandbox** to support the model invoking code tools during reasoning for image operations (cropping, zooming, enhancement, annotation, etc.). #### Step 1: Deploy the Code Sandbox We recommend deploying the AIO Sandbox with Docker: ```bash git clone https://github.com/agent-infra/sandbox cd sandbox # Mount the host image directory into the container docker run -d \ --name twi-sandbox \ -p 18081:18081 \ -v /data/images:/mnt/data/images \ # host path β†’ sandbox path sandbox:latest ``` The mount path must match the path configuration in the FastAPI service. #### Step 2: Start the Thinking with Images FastAPI Service Download [twi_server.py](twi_server.py) and update the path configuration at the top of the file: ```python CHAT_API = "http://localhost:9200/v1/chat/completions" # vLLM address JUPYTER_API = "http://localhost:18081/v1/jupyter" # Sandbox address HOST_IMG_DIR = "/data/images" # ← Host image directory (must match docker -v mount) ``` Start the service: ```bash pip install fastapi uvicorn httpx pillow python twi_server.py # Listens on port 10044 ``` #### Step 3: Call the Thinking with Images Endpoint ```python import httpx import base64 with open("path/to/your/image.png", "rb") as f: image_b64 = base64.b64encode(f.read()).decode("utf-8") messages = [ {"type": "text", "text": "Please carefully analyze this scientific image."}, {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_b64}"}}, ] response = httpx.post( "http://localhost:10044/process", json={ "messages": messages, "image_path_list": ["/data/images/your_image.png"], # Absolute host path }, timeout=300, ) result = response.json() # The final answer is the last message with role="assistant" final = [m for m in result["messages"] if m["role"] == "assistant"][-1] print(final["content"]) ``` ## πŸ“„ Citation If you use S1-VL-32B in your research, please cite (the corresponding paper is coming soon): ```latex @misc{s1vl2026, title = {S1-VL-32B: Scientific Multimodal Reasoning Model}, author = {ScienceOne Team}, year = {2026}, howpublished = {\url{https://huggingface.co/ScienceOne-AI/S1-VL-32B}} } ``` ## πŸ“œ License This project is released under the Apache 2.0 License. ## πŸ™ Acknowledgements We thank the open-source communities and pioneering works of [Qwen3-VL](https://modelscope.cn/collections/Qwen3-VL-5c7a94c8cb144b) and [AIO Sandbox](https://github.com/agent-infra/sandbox) for laying the foundation for the scientific multimodal reasoning research behind S1-VL-32B.