unsloth/Seed-Coder-8B-Instruct-GGUF
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Seed-Coder-8B-Instruct
<div align="left" style="line-height: 1;"> <a href="https://bytedance-seed-coder.github.io/" target="_blank" style="margin: 2px;"> <img alt="Homepage" src="https://img.shields.io/badge/Seed--Coder-Homepage-a468fe?color=a468fe&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a>
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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.
<p align="center"> <img width="100%" src="imgs/seed-coderintroperformance.png"> </p>
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 accelerateQuickstart
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},
}