Advanced Open-Source Code LLM by ByteDance Seed
A powerful family of large language models specialized for coding, offering base, instruct, and reasoning variants at 8B scale. Designed to advance code intelligence through AI-driven solutions.
Foundational model providing general coding capabilities and serving as the core architecture for specialized variants.
Optimized for following specific coding instructions, excelling in tasks requiring precise implementation of requirements.
Specialized in complex problem-solving and algorithms, ideal for competitive programming and advanced algorithmic tasks.
Leverages LLMs for data curation, minimizing manual effort in pretraining data construction and streamlining development.
Openly shares detailed insights into data pipeline and curation methods, fostering trust and enabling community replication.
Aims for state-of-the-art performance among open-source models, supported by strong benchmark results.
Balanced performance and efficiency for optimal coding assistance, positioning it as a mid-range model in terms of computational complexity.
Available on GitHub and Hugging Face for community access, fostering broader applications and community engagement.
Advanced data curation from GitHub, commits, and code-related web data using LLMs, ensuring high-quality training data.
Detailed technical report available for in-depth understanding of methodologies, architecture specifics, and experimental results.
Seed Coder is a family of open-source code large language models (LLMs) developed by ByteDance Seed. It's designed specifically for coding tasks and comes in three variants: base, instruct, and reasoning, all at 8B scale.
The model comes in three specialized variants: Base (general coding capabilities), Instruct (optimized for following specific coding instructions), and Reasoning (focused on complex problem-solving and algorithms). Each variant is optimized for different coding needs.
Seed Coder shows strong performance among open-source models of similar size. It excels in benchmarks like SWE-bench Verified and Multi-SWE-bench mini, and even outperforms some larger models in Agentless workflows. The Reasoning variant has shown impressive results in competitive programming tasks.
Seed Coder stands out for its model-centric approach to data curation, complete transparency in its data pipeline, and strong performance in coding tasks. It uses LLMs for data filtering, reducing manual effort while maintaining high quality.
Seed Coder is open-source and available on both GitHub and Hugging Face. You can download the models, access the technical documentation, and integrate them into your development workflow. The project includes comprehensive documentation for implementation.
As an 8B parameter model, Seed Coder requires appropriate computational resources. The exact requirements depend on your use case, but it's designed to balance performance and efficiency, making it suitable for various deployment scenarios.
Seed Coder uses a model-centric approach for data curation, leveraging LLMs to process data from GitHub, commits, and code-related web sources. The development process is transparent, with detailed documentation available in the technical report.
Seed Coder can be used for various coding tasks including code generation, debugging, and educational purposes. Its different variants make it suitable for both basic coding assistance and complex algorithmic problem-solving.
Yes, Seed Coder has an active open-source community. You can find support through GitHub discussions, community forums, and the project's documentation. The community actively contributes to improvements and adaptations of the model.
Seed Coder aims to continue advancing code intelligence through community contributions and updates. The project focuses on improving performance, expanding capabilities, and fostering broader applications in the coding community.
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