Seed-Coder

Seed-Coder is a large open source code language model series launched by ByteDance Seed team, which includes basic, instruction and inference models. It aims to independently manage code training data through minimal human investment, thereby significantly improving programming capabilities. This model performs superiorly among similar open source models, is suitable for various coding tasks, is positioned to promote the development of the open source LLM ecosystem, and is suitable for research and industry.

Demand population:

"This product is suitable for developers, data scientists and AI researchers who require efficient code generation and understanding. With its powerful model performance and flexibility, Seed-Coder can help them quickly implement functionality in projects and optimize development processes."

Example of usage scenarios:

Use Seed-Coder to generate efficient sorting algorithms.

Use Seed-Coder for code completion to improve development efficiency.

Use Seed-Coder for code reconstruction and optimization in software engineering projects.

Product Features:

Model-centered data screening to reduce manual work.

Publicly share transparent processes for data processing and model training.

Excellent performance in a variety of coding tasks and reaching industry-leading levels.

Supports context input up to 32K to adapt to complex coding scenarios.

A variety of model types are provided to meet different needs, including foundation, instructions, and reasoning.

Supports multi-GPU distributed inference to improve service performance.

Tutorials for use:

Visit the GitHub page of Seed-Coder and download the corresponding model.

Choose the appropriate Seed-Coder model (basic, instruction or reasoning) according to project requirements.

Configure and initialize the model using the provided sample code.

Enter the corresponding encoding prompt according to the specific requirements.

Run the model to generate code and test its output results.