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
```

yuyuzhang commited on May 9, 2025

Commit

7935afb

verified ·

1 Parent(s): 40743b7

Update README.md

Browse files

Files changed (1) hide show

README.md +9 -8

README.md CHANGED Viewed

@@ -11,21 +11,22 @@ We are thrilled to introduce Seed-Coder, a powerful, transparent, and parameter-
 - 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.
 ## Highlight
-**Seed-Coder-8B-Base** is an 8-billion-parameter foundation model tailored for code understanding and generation. It is designed to provide developers with a powerful, general-purpose code model capable of handling a wide range of coding tasks. It features:
-- Pretrained on a **massively curated corpus**, filtered using **LLM-based techniques** to ensure high-quality real-world code, resulting in cleaner and more effective learning signals.
-- Excels at **code completion** and supports **Fill-in-the-Middle (FIM)** tasks, enabling it to predict missing code spans given partial contexts.
-- Robust performance across **various programming languages** and **code reasoning scenarios**, making it ideal for downstream finetuning or direct use in code generation systems.
-- **Long-context support** up to 32K tokens, enabling it to handle large codebases, multi-file projects, and extended editing tasks.
 Seed-Coder-8B-Base serves as the foundation for Seed-Coder-8B-Instruct and Seed-Coder-8B-reasoning.
-<p align="center">
-  <img width="100%" src="imgs/seed-coder_intro_performance.jpg">
-</p>
 ## Model Downloads
 | Model Name                  | Length | Download   |    Notes |

 - 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>
 ## Highlight
+Seed-Coder-8B-Base is an 8-billion-parameter foundation model tailored for code understanding and generation. It is designed to provide developers with a powerful, general-purpose code model capable of handling a wide range of coding tasks. It features:
+- Pretrained on a massively curated corpus, filtered using **LLM-based techniques** to ensure high-quality real-world code, resulting in cleaner and more effective learning signals.
+- Excels at code completion and supports Fill-in-the-Middle (FIM) tasks, enabling it to predict missing code spans given partial contexts.
+- Robust performance across various programming languages, making it ideal for downstream finetuning or direct use in code generation systems.
+- Long-context support up to 32K tokens, enabling it to handle large codebases, multi-file projects, and extended editing tasks.
 Seed-Coder-8B-Base serves as the foundation for Seed-Coder-8B-Instruct and Seed-Coder-8B-reasoning.
 ## Model Downloads
 | Model Name                  | Length | Download   |    Notes |