For developers, the following steps can be followed for local deployment of DeepSeek-R1 WebGPU projects:
1. Environmental preparation
- Ensure that Node.js and the npm environment are installed on the system
- Preparing modern browsers with WebGPU support
2. Access to project code
- Clone the project repository via Git:
git clone https://github.com/huggingface/transformers.js-examples.git - Go to the project catalog:
cd transformers.js-examples/deepseek-r1-webgpu
3. Installation of dependencies
- Execute the npm install command:
npm i
4. Running the development environment
- Start the development server:
npm run dev - Accessed in a browser:
http://localhost:5173
5. Understanding of project structure
- The core logic is located in transformers.js
- WebGPU acceleration is handled automatically by the underlying framework
- Model weights are automatically downloaded and cached
6. Customization development
- Front-end interface can be modified to suit specific needs
- May try to integrate DeepSeek models at different scales
- Be careful to comply with the license agreement of the open source project
This deployment process enables developers to quickly establish a local AI development environment, laying the foundation for subsequent model customization and application development.
This answer comes from the articleDeepSeek-R1 WebGPU: Run DeepSeek R1 1.5B locally in your browser!The
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