README.md

---
license: mit
base_model:
- unsloth/Seed-Coder-8B-Instruct
pipeline_tag: text-generation
library_name: rkllm
tags:
- unsloth
- rkllm
- rk3588
base_model_relation: quantized
---
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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>

  <a href="https://github.com/ByteDance-Seed/Seed-Coder/blob/master/Seed-Coder.pdf" target="_blank" style="margin: 2px;">
    <img alt="Technical Report" src="https://img.shields.io/badge/(upcoming)-Technical%20Report-brightgreen?logo=arxiv&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
  </a>
  
  <a href="https://huggingface.co/ByteDance-Seed" target="_blank" style="margin: 2px;">
      <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-ByteDance%20Seed-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
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</div>


## 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.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
| 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            | 64K    | 🤗 [Model](https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Reasoning)   |  RL trained to boost reasoning capabilities.  |
| Seed-Coder-8B-Reasoning-bf16 | 64K    | 🤗 [Model](https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Reasoning-bf16)   |  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 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](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 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}, 
}
``` -->