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ByteDance-Seed/
Seed-Coder-8B-Instruct

Name: Seed-Coder-8B-Instruct
Rating: 2.5 (3235 reviews)

ByteDance-Seed/Seed-Coder-8B-Base

finetune

mit

autotrain_compatible

text-generation-inference

endpoints_compatible

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Seed-Coder-8B-Instruct

Technical Report

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

Model Name	Length	Download	Notes
Seed-Coder-8B-Base	32K	🤗 Model	Pretrained on our model-centric code data.
👉 Seed-Coder-8B-Instruct	32K	🤗 Model	Instruction-tuned for alignment with user intent.
Seed-Coder-8B-Reasoning	64K	🤗 Model	RL trained to boost reasoning capabilities.
Seed-Coder-8B-Reasoning-bf16	64K	🤗 Model	RL trained to boost reasoning capabilities.

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:

python

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.

Model	HumanEval	MBPP	MHPP	BigCodeBench (Full)	BigCodeBench (Hard)	LiveCodeBench (2410 – 2502)
CodeLlama-7B-Instruct	40.9	54.0	6.7	25.7	4.1	3.6
DeepSeek-Coder-6.7B-Instruct	74.4	74.9	20.0	43.8	15.5	9.6
CodeQwen1.5-7B-Chat	83.5	77.7	17.6	43.6	15.5	3.0
Yi-Coder-9B-Chat	82.3	82.0	26.7	49.0	17.6	17.5
Llama-3.1-8B-Instruct	68.3	70.1	17.1	40.5	13.5	11.5
OpenCoder-8B-Instruct	83.5	79.1	30.5	50.9	18.9	17.1
Qwen2.5-Coder-7B-Instruct	88.4	83.5	26.7	48.8	20.3	17.3
Qwen3-8B	84.8	77.0	32.8	51.7	23.0	23.5
Seed-Coder-8B-Instruct	84.8	85.2	36.2	53.3	26.4	24.7

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.

@misc{seed2025seedcoderletcodemodel,
      title={{Seed-Coder}: Let the Code Model Curate Data for Itself}, 
      author={{ByteDance Seed} and Yuyu Zhang and Jing Su and Yifan Sun and Chenguang Xi and Xia Xiao and Shen Zheng and Anxiang Zhang and Kaibo Liu and Daoguang Zan and Tao Sun and Jinhua Zhu and Shulin Xin and Dong Huang and Yetao Bai and Lixin Dong and Chao Li and Jianchong Chen and Hanzhi Zhou and Yifan Huang and Guanghan Ning and Xierui Song and Jiaze Chen and Siyao Liu and Kai Shen and Liang Xiang and Yonghui Wu},
      year={2025},
      eprint={2506.03524},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.03524}, 
}