Prince-1/Seed-Coder-8B-Instruct

*[Unsloth Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf) achieves superior accuracy & outperforms other leading quants.*



[![](https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png)](https://github.com/unslothai/unsloth/)
[![](https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png)](https://discord.gg/unsloth)
[![](https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png)](https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune)

Seed-Coder-8B-Instruct

Introduction

We are thrilled to introduce Seed-Coder, a powerful, transparent, and parameter-efficient family of open-source code models at the 8B scale, featuring base, instruct, and reasoning variants. Seed-Coder contributes to promote the evolution of open code models through the following highlights.

• Model-centric: Seed-Coder predominantly leverages LLMs instead of hand-crafted rules for code data filtering, minimizing manual effort in pretraining data construction.

• Transparent: We openly share detailed insights into our model-centric data pipeline, including methods for curating GitHub data, commits data, and code-related web data.

• Powerful: Seed-Coder achieves state-of-the-art performance among open-source models of comparable size across a diverse range of coding tasks.

  

This repo contains the Seed-Coder-8B-Instruct model, which has the following features:

• Type: Causal language models
• Training Stage: Pretraining & Post-training
• Data Source: Public datasets, synthetic data
• Context Length: 32,768

Model Downloads

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 generate code using the Hugging Face pipeline API:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "ByteDance-Seed/Seed-Coder-8B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)

messages = [
    {"role": "user", "content": "Write a quick sort algorithm."},
]

input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    return_tensors="pt",
    add_generation_prompt=True,  
).to(model.device)

outputs = model.generate(input_ids, max_new_tokens=512)
response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
print(response)

Evaluation

Seed-Coder-8B-Instruct has been evaluated on a wide range of coding tasks, including code generation, code reasoning, code editing, and software engineering, achieving state-of-the-art performance among ~8B open-source models.

For detailed benchmark performance, please refer to our 📑 Technical Report.

License

This project is licensed under the MIT License. See the LICENSE file for details.

<!-- ## 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}, 
}
``` -->