---
license: mit
base_model:
- ByteDance-Seed/Seed-Coder-8B-Base
---

# Seed-Coder-8B-Instruct

## Introduction
Seed-Coder-8B-Instruct is an 8-billion-parameter model instruction-tuned specifically for code generation, code reasoning, and code understanding. It is built to empower developers with high-quality, efficient code assistance. It features:
- Trained on a **massively curated corpus**, where **an LLM-based filter** is applied to select **high-quality real-world code**, **text-code alignment data**, and **synthetic datasets** — ensuring cleaner and more useful data compared to traditional heuristic-based curation.
- Achieves superior performance across **code generation**, **bug fixing**, and **reasoning** tasks, rivaling or surpassing larger open-source code models.
- **Instruction-tuned** to reliably follow user intents across a diverse range of coding and reasoning prompts.
- Supports **long-context handling** up to 32K tokens, enabling processing of complex multi-file projects and detailed coding tasks.

<p align="center">
  <img width="100%" src="imgs/large_model_comparison_split-crop.png">
</p>

## 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 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 demonstrates strong performance across a variety of coding benchmarks, showing:
- Competitive or superior results compared to similarly sized open-source code models.
- Robustness across different programming languages and domains.
- Ability to understand, reason, and repair complex code snippets.

|             Model             | HumanEval | MBPP | MHPP | BigCodeBench (Full) | BigCodeBench (Hard) | LiveCodeBench (2410 – 2502) |
|:-----------------------------:|:---------:|:----:|:----:|:-------------------:|:-------------------:|:-------------------------:|
|     CodeLlama-7B-Instruct     |    40.9   | 54.0 |  6.7 |         21.9        |         3.4         |            3.6            |
|  DeepSeek-Coder-6.7B-Instruct |    74.4   | 74.9 | 20.0 |         35.5        |         10.1        |            9.6            |
|      CodeQwen1.5-7B-Chat      |    83.5   | 77.7 | 17.6 |         39.6        |         18.9        |            3.0            |
|        Yi-Coder-9B-Chat       |    82.3   | 82.0 | 26.7 |         38.1        |         11.5        |            17.5           |
|     Llama-3.1-8B-Instruct     |    68.3   | 70.1 | 17.1 |         36.6        |         13.5        |            11.5           |
|     OpenCoder-8B-Instruct     |    83.5   | 79.1 | 30.5 |         40.3        |         16.9        |            17.1           |
|   Qwen2.5-Coder-7B-Instruct   |    88.4   | 82.0 | 26.7 |         41.0        |         18.2        |            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        |         20.5        |            24.7           |


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

<!-- ## 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}, 
}
``` -->