Instructions to use ByteDance-Seed/Seed-Coder-8B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ByteDance-Seed/Seed-Coder-8B-Base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ByteDance-Seed/Seed-Coder-8B-Base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ByteDance-Seed/Seed-Coder-8B-Base")
model = AutoModelForCausalLM.from_pretrained("ByteDance-Seed/Seed-Coder-8B-Base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ByteDance-Seed/Seed-Coder-8B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ByteDance-Seed/Seed-Coder-8B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ByteDance-Seed/Seed-Coder-8B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ByteDance-Seed/Seed-Coder-8B-Base

SGLang

How to use ByteDance-Seed/Seed-Coder-8B-Base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ByteDance-Seed/Seed-Coder-8B-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ByteDance-Seed/Seed-Coder-8B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ByteDance-Seed/Seed-Coder-8B-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ByteDance-Seed/Seed-Coder-8B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ByteDance-Seed/Seed-Coder-8B-Base with Docker Model Runner:
```
docker model run hf.co/ByteDance-Seed/Seed-Coder-8B-Base
```

Seed-Coder-8B-Base / README.md

yuyuzhang

Update README.md

049f843 verified 11 months ago

preview code

raw

history blame contribute delete

6.95 kB

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

	# Seed-Coder-8B-Base

	<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://arxiv.org/abs/2506.03524" target="_blank" style="margin: 2px;">
	<img alt="Technical Report" src="https://img.shields.io/badge/arXiv-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;"/>
	</a>

	<a href="https://github.com/ByteDance-Seed/Seed-Coder/blob/master/LICENSE" style="margin: 2px;">
	<img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?color=f5de53&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
	</a>
	</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-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 \| 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 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 \| 54.8 \|

	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:

	```
	@misc{seed2025seedcoderletcodemodel,
	title={{Seed-Coder}: Let the Code Model Curate Data for Itself},
	author={{ByteDance Seed} and Yuyu Zhang and Jing Su and Yifan Sun and Chenguang Xi and Xia Xiao and Shen Zheng and Anxiang Zhang and Kaibo Liu and Daoguang Zan and Tao Sun and Jinhua Zhu and Shulin Xin and Dong Huang and Yetao Bai and Lixin Dong and Chao Li and Jianchong Chen and Hanzhi Zhou and Yifan Huang and Guanghan Ning and Xierui Song and Jiaze Chen and Siyao Liu and Kai Shen and Liang Xiang and Yonghui Wu},
	year={2025},
	eprint={2506.03524},
	archivePrefix={arXiv},
	primaryClass={cs.CL},
	url={https://arxiv.org/abs/2506.03524},
	}
	```