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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

nielsr HF Staff

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	---
	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://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;"/>
	</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.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},
	}
	``` -->