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Seed-Coder - AI Language Models Tool

Overview

Seed-Coder is an open-source family of code-specialized language models (approximately 8 billion parameters) developed by ByteDance Seed. The project ships three purpose-built variants — Base, Instruct, and Reasoning — to target core code workflows: general code modeling, instruction-following code generation, and advanced algorithmic reasoning (the Reasoning variant offers an extended long context). Seed-Coder was designed around a "model-centric" data pipeline: smaller LLMs are used to filter and curate raw code sources (GitHub, commit histories, and web-crawled code) into a high-quality training corpus, rather than relying on hand-crafted heuristics. ([github.com](https://github.com/ByteDance-Seed/Seed-Coder?utm_source=openai)) The project emphasizes transparency and reproducibility: code, technical report, and Hugging Face model releases are publicly available, and the team published evaluation results across multiple code benchmarks showing competitive performance among ~8B open-source models. Seed-Coder was released in May 2025 with follow-up clarifications to evaluation settings; model artifacts (including BF16 variants and long-context builds) are hosted on Hugging Face for direct download and inference. ([github.com](https://github.com/ByteDance-Seed/Seed-Coder?utm_source=openai))

GitHub Statistics

  • Stars: 744
  • Forks: 54
  • Contributors: 3
  • License: MIT
  • Last Updated: 2025-06-06T02:10:40Z

According to the official GitHub repository, Seed-Coder is MIT-licensed and shows active project publication with a focused contributor base; repository metadata (stars/forks/contributors) indicates early-stage community adoption. The repository lists technical documentation, a PDF technical report, and example inference code for transformers and vLLM. Recent project news in the README (release and an evaluation-setting update) suggests the maintainers are actively curating experimental results and model artifacts. ([github.com](https://github.com/ByteDance-Seed/Seed-Coder?utm_source=openai))

Installation

Install via pip:

git clone https://github.com/ByteDance-Seed/Seed-Coder.git
pip install -U transformers accelerate
pip install vllm  # optional, for vLLM-based inference

Key Features

  • Model-centric data curation: LLM filters curate GitHub, commits, and web code to build cleaner pretraining corpora. ([seed.bytedance.com](https://seed.bytedance.com/en/blog/seed-coder-open-sourced-llm-based-code-data-building-method-validated?utm_source=openai))
  • Three 8B variants: Base (32K context), Instruct (32K context), Reasoning (64K long-context). ([github.com](https://github.com/ByteDance-Seed/Seed-Coder?utm_source=openai))
  • Instruction-tuned Instruct variant optimized for user intent and code generation tasks. ([huggingface.co](https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Instruct?utm_source=openai))
  • Reinforcement-learned Reasoning model: RL-finetuning to boost algorithmic reasoning and competitive programming performance. ([github.com](https://github.com/ByteDance-Seed/Seed-Coder?utm_source=openai))
  • Benchmarked performance: strong HumanEval and MBPP results versus other ~8B open-source code models. ([huggingface.co](https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Instruct?utm_source=openai))

Community

Seed-Coder has visible traction on Hugging Face (model pages, downloads and a project collection) and an official ByteDance Seed announcement; however, the GitHub repo shows a small core contributor count indicating an early-stage open-source community. The team published a technical report and continues to publish model variants (including BF16 and long-context builds) in response to user feedback. For community resources and model downloads, refer to the Hugging Face model pages and the GitHub repository. ([huggingface.co](https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Instruct?utm_source=openai))

Last Refreshed: 2026-02-24

Key Information

  • Category: Language Models
  • Type: AI Language Models Tool
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