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