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Seed Coder
Overview :
Seed-Coder is a series of open-source code large language models launched by the Seed Team of ByteDance. It includes base, instruction, and reasoning models, aiming to significantly enhance programming capabilities through minimal human effort and autonomous management of code training data. The model performs excellently among similar open-source models and is suitable for various coding tasks. It is positioned to promote the development of the open-source LLM ecosystem and is applicable to both research and industry.
Target Users :
This product is suitable for developers, data scientists, and AI researchers who need efficient code generation and understanding capabilities. Seed-Coder, with its powerful model performance and flexibility, can help them quickly implement features and optimize the development process in their projects.
Use Cases
Use Seed-Coder to generate efficient sorting algorithms.
Use Seed-Coder for code completion to improve development efficiency.
Utilize Seed-Coder for code refactoring and optimization in software engineering projects.
Features
Data filtering centered around models to reduce manual work.
Publicly sharing transparent processes for data processing and model training.
Outstanding performance in multiple coding tasks, reaching industry-leading levels.
Supports up to 32K context input, adapting to complex coding scenarios.
Provides various model types to meet different needs, including base, instruction, and reasoning.
Supports multi-GPU distributed reasoning to improve service performance.
How to Use
Visit the Seed-Coder GitHub page and download the corresponding model.
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Choose an appropriate Seed-Coder model (base, instruction, or reasoning) based on project requirements.
Configure and initialize the model using the provided example codes.
Input corresponding coding prompts according to specific needs.
Run the model to generate code and test its output results.
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