license: mit

Seed-Coder-8B-Base

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-Base model, with the following features: - Type: Causal language models - Training Stage: Pretraining - Data Source: GitHub data, code-related web data - Training Tokens: 6 trillion - Supports: Code completion, code infilling (Fill-in-the-Middle) - 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 perform code generation using the Hugging Face pipeline API:

import transformers
import torch

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

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

output = pipeline("def say_hello_world():", max_new_tokens=100)
print(output[0]["generated_text"])

Fill-in-the-Middle (FIM) Example

Seed-Coder-8B-Base natively supports Fill-in-the-Middle (FIM) tasks, where the model is given a prefix and a suffix and asked to predict the missing middle content. This allows for code infilling scenarios such as completing a function body or inserting missing logic between two pieces of code.

A typical example:

import transformers
import torch

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

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

# You can concatenate a prefix, a special FIM separator token, and a suffix
prefix = "def add_numbers(a, b):\n    "
suffix = "\n    return result"

# Combine prefix and suffix following the FIM format
fim_input = '<[fim-suffix]>' + suffix + '<[fim-prefix]>' + prefix + '<[fim-middle]>'

output = pipeline(fim_input, max_new_tokens=512)
print(output[0]["generated_text"])

Evaluation

Seed-Coder-8B-Base has been evaluated on code generation, code completion, and code reasoning benchmarks, 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 Seed-Coder helpful, please consider citing our work:

@article{bytedance2025seedcoder, title={Seed-Coder: Let the Code Model Curate Data for Itself}, author={Xxx}, year={2025}, eprint={xxxx.xxxxx}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/xxxx.xxxxx}, } –>