license: apache-2.0
base_model:
- ByteDance-Seed/Seed-Coder-8B-Base
Seed-Coder-8B-Reasoning
Introduction
Seed-Coder-8B-Reasoning is an 8-billion-parameter model further optimized for code reasoning, problem-solving, and algorithmic thinking tasks.
Built upon the strong base of Seed-Coder, it undergoes additional training in sandbox environments to significantly enhance its ability to tackle complex coding problems and competitions. It features:
- Trained on a massively curated corpus, filtered using an LLM-based method to ensure high-quality real-world code, text-code alignment, and synthetic datasets.
- Sandbox fine-tuning to specifically strengthen multi-step reasoning, algorithm design, and competitive programming capabilities.
- Maintains long-context handling up to 32K tokens, enabling it to reason over extended problem descriptions and large input-output examples.
Model Downloads
| Model Name | Type | Length | Download |
|---|---|---|---|
| Seed-Coder-8B-Base | base | 32k | 🤗 Hugging Face |
| Seed-Coder-8B-Instruct | instruct | 32k | 🤗 Hugging Face |
| 👉Seed-Coder-8B-Reasoning | reasoning | 32k | 🤗 Hugging Face |
Requirements
You will need to install the latest versions of transformers and accelerate:
pip install -U transformers accelerate
Quickstart
Here is a simple example demonstrating how to load the model and perform code generation using the Hugging Face pipeline API:
import transformers
import torch
model_id = "ByteDance-Seed/Seed-Coder-8B-Reasoning"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "user", "content": "Solve the following problem: Given an array of integers, find two numbers such that they add up to a specific target number."},
]
outputs = pipeline(
messages,
max_new_tokens=512,
)
print(outputs[0]["generated_text"][-1]["content"])
Evaluation
Seed-Coder-8B-Reasoning has been evaluated extensively on reasoning-intensive code benchmarks, showing:
- Significant improvements on competitive programming datasets and coding challenges.
- Enhanced ability to break down complex problems, design correct algorithms, and produce efficient implementations.
- Strong generalization to unseen problems across multiple domains (math, strings, arrays, graphs, DP, etc.).
For detailed results, please check our 📑 paper.
Citation
If you find our work helpful, please consider citing our work:
@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},
}