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--- |
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license: apache-2.0 |
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base_model: |
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- ByteDance-Seed/Seed-Coder-8B-Base |
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--- |
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# Seed-Coder-8B-Instruct |
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## Introduction |
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Seed-Coder-8B-Instruct is an 8-billion-parameter model instruction-tuned specifically for code generation, code reasoning, and code understanding. It is built to empower developers with high-quality, efficient code assistance. It features: |
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- Trained on a **massively curated corpus**, where **an LLM-based filter** is applied to select **high-quality real-world code**, **text-code alignment data**, and **synthetic datasets** — ensuring cleaner and more useful data compared to traditional heuristic-based curation. |
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- Achieves superior performance across **code generation**, **bug fixing**, and **reasoning** tasks, rivaling or surpassing larger open-source code models. |
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- **Instruction-tuned** to reliably follow user intents across a diverse range of coding and reasoning prompts. |
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- Supports **long-context handling** up to 32K tokens, enabling processing of complex multi-file projects and detailed coding tasks. |
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## Requirements |
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You will need to install the latest versions of `transformers` and `accelerate`: |
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```bash |
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pip install -U transformers accelerate |
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``` |
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## Quickstart |
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Here is a simple example demonstrating how to load the model and generate code using the Hugging Face `pipeline` API: |
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```python |
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import transformers |
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import torch |
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model_id = "ByteDance-Seed/Seed-Coder-8B-Instruct" |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model_id, |
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model_kwargs={"torch_dtype": torch.bfloat16}, |
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device_map="auto", |
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) |
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messages = [ |
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{"role": "user", "content": "Write a quick sort algorithm."}, |
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] |
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outputs = pipeline( |
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messages, |
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max_new_tokens=512, |
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) |
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print(outputs[0]["generated_text"][-1]["content"]) |
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``` |
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## Evaluation |
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Seed-Coder-8B-Instruct demonstrates strong performance across a variety of coding benchmarks, showing: |
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- Competitive or superior results compared to similarly sized open-source code models. |
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- Robustness across different programming languages and domains. |
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- Ability to understand, reason, and repair complex code snippets. |
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For detailed results, please check our [📑 paper](https://arxiv.org/pdf/xxx.xxxxx). |
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## Citation |
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If you find our work helpful, feel free to give us a cite. |
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``` |
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@article{zhang2025seedcoder, |
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title={Seed-Coder: Let the Code Model Curate Data for Itself}, |
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author={Xxx}, |
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year={2025}, |
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eprint={2504.xxxxx}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/xxxx.xxxxx}, |
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} |
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``` |
