Skywork-R1V: A Graphical Hybrid Multimodal Reasoning Model Open Source by Kunlun Wanwen

General Introduction

Skywork-R1V is an open source multimodal reasoning model developed by the SkyworkAI (Kunlun Wanwei) team and published on GitHub.It is capable of processing images and text simultaneously, performing multi-step logical reasoning, and is particularly good at analyzing complex image problems. The model was officially launched on March 18, 2025 with a parameter size of 3.8 billion. It supports Chain-of-Thought (Chain-of-Thought), which can step-by-step decompose image content to help users solve problems in math, science, etc. Skywork-R1V aims to promote the development of AI technology and allow more people to use powerful reasoning tools for free. It is not only powerful, but also provides detailed documentation and code for developers to use and improve.

Function List

• Visual Thinking Chain Reasoning: The ability to analyze the content of images step-by-step, break down complex questions, and provide clear answers.
• Math problem solving: Recognize math problems in images and give high-precision answers.
• Scientific Image Interpretation: Analyze medical or scientific images to extract key information.
• cross-modal understanding: Combine text and images to provide more comprehensive reasoning results.
• Open Source Support: Provides complete code and models, allowing users to modify and deploy freely.

Using Help

Skywork-R1V is an open source project, users need to download it via GitHub and configure the environment locally to use it. Below is a detailed guide to help you get started quickly.

Installation process

1. Preparing the environment
   • Make sure you have Python 3.8 or above installed on your computer. This can be done with the command python --version Check.
   • Git needs to be installed to download the code; Windows users can download it from the official website, Linux or Mac users can download it from the terminal by typing sudo apt install git maybe brew install git Installation.
   • A GPU environment (e.g. NVIDIA graphics card) is recommended to improve performance, and CUDA and cuDNN need to be installed.
2. Download Code
   • Open a terminal or command line and enter the following command to clone the repository:

     git clone https://github.com/SkyworkAI/Skywork-R1V.git
     

   • Go to the project folder:

     cd Skywork-R1V
     

3. Installation of dependencies
   • The project provides a dependency file . Run the following command to install the required libraries:

     pip install -r requirements.txt
     

   • If you need to speed up reasoning, install Flash Attention:

     pip install flash-attn --no-build-isolation
     

4. Download model
   • The model files for Skywork-R1V are hosted on Hugging Face. Access https://huggingface.co/Skywork/Skywork-R1V-38B, download the model file manually, or use the following command:

     huggingface-cli download Skywork/Skywork-R1V-38B --local-dir . /model
     

   • Place the downloaded model files in the project directory under the model Folder.
5. Configuring the runtime environment
   • If there is more than one GPU, set the visible devices. For example, use two GPUs:

     export CUDA_VISIBLE_DEVICES="0,1"
     

How to use the main features

The core function of Skywork-R1V is reasoning through images and text. The following is the operation procedure.

Function 1: Visual Thought Chain Reasoning

• Prepare to enter: Save images to be analyzed (e.g., math topics or scientific diagrams) locally, for example image1.jpgThe
• Preparation of questions: Specify the question in the code. For example, you want to ask "What is the answer to the math problem in the picture?". .
• running inference:: Editorial  file, fill in the image path and question:

  image_paths = ["image1.jpg"]
  question = "What is the answer to the math question in the image?"

• execute a command: Runs in the terminal:

  python inference_with_transformers.py ---model_path . /model --image_paths image1.jpg --question "What is the answer to the math question in the image?"
  

• View Results: The program outputs the step-by-step reasoning process and the final answer.

Function 2: Math problem solving

• input image: Upload images that contain math formulas, such as handwritten or printed titles.
• running code: Similar to the visual chain of thought, set the problem to "Solve the math problem in the picture" and run it:

  python inference_with_transformers.py ---model_path . /model --image_paths math_image.jpg --question "Solve the math problem in the image"
  

• Results Showcase: The model recognizes the formula, calculates it step by step, and finally gives the answer.

Function 3: Scientific Image Interpretation

• Upload a picture: Prepare medical images or scientific charts, such as x-rays or cell microscope images.
• ask questions: Enter specific questions such as "What is the cell structure in the picture?" .
• running program::

  python inference_with_transformers.py ---model_path . /model --image_paths science_image.jpg --question "What is the cell structure in the image?"
  

• output analysis: The model will extract the image features and give a detailed explanation in conjunction with the problem.

Handling Precautions

• picture format: Common formats such as JPG, PNG are supported, and high image clarity is recommended.
• hardware requirement: Runs on computers without GPUs, but is slower. At least 16GB of RAM is recommended.
• Debugging Issues: If you encounter an error, check the  for a complete installation, or check the Issues page on GitHub for help.

With the above steps, you can easily use Skywork-R1V to process image and text tasks. For more advanced usage, you can refer to the official documentation The

application scenario

1. Educational aids
   Students can use Skywork-R1V to analyze picture topics in their math homework to get quick answers and steps to solve the problems and help understand the knowledge.
2. scientific research
   Researchers can upload images of their experiments to allow the model to interpret the data or image content, saving analysis time.
3. Medical support
   Doctors can input X-ray or microscope images for preliminary diagnostic recommendations, improving work efficiency.

QA

1. What languages does Skywork-R1V support?
   Currently, it mainly supports Chinese and English, and both text input and output can be in these two languages.
2. Do I have to pay for it?
   No. Skywork-R1V is completely open source and the code and models are available for free.
3. Does it work without a GPU?
   It is possible, but reasoning will be much slower. It is recommended to reduce the image resolution when using CPU.