README.md

---
license: mit
library_name: transformers
pipeline_tag: text-generation
---

# Seed-Coder-8B-Base

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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-coder_intro_performance.jpg">
</p>

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
| Model Name                  | Length | Download   |    Notes |
|---------------------------------------------------------|--------|------------------------------------|-----------------------|
| 👉 **Seed-Coder-8B-Base**           | 32K    | 🤗 [Model](https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base)   |  Pretrained on our model-centric code data.  |
| Seed-Coder-8B-Instruct             | 32K    | 🤗 [Model](https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Instruct)   |  Instruction-tuned for alignment with user intent. |
| Seed-Coder-8B-Reasoning            | 32K    | 🤗 [Model](https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Reasoning)   |  RL trained to boost reasoning capabilities.  |


## Requirements
You will need to install the latest versions of `transformers` and `accelerate`:

```bash
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:

```python
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:

```python
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.

|            | DeepSeek-Coder-6.7B-Base | OpenCoder-8B-Base | Qwen2.5-Coder-7B | Seed-Coder-8B-Base |
|------------|:------------------------:|:-----------------:|:----------------:|:------------------:|
| HumanEval  |           47.6           |        66.5       |       72.0       |        77.4        |
| MBPP       |           70.2           |        79.9       |       79.4       |        82.0        |
| MultiPL-E  |           44.7           |        61.0       |       58.8       |        67.6        |
| cruxeval-O |           41.0           |        43.9       |       56.0       |        48.4        |

For detailed benchmark performance, please refer to our [📑 Technical Report](https://github.com/ByteDance-Seed/Seed-Coder/blob/master/Seed-Coder.pdf).


## License

This project is licensed under the MIT License. See the [LICENSE file](https://github.com/ByteDance-Seed/Seed-Coder/blob/master/LICENSE) 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}, 
}
```  -->