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
```

yuyuzhang commited on May 9, 2025

Commit

5ed1529

verified ·

1 Parent(s): bb46339

Update README.md

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README.md +3 -8

README.md CHANGED Viewed

@@ -34,7 +34,7 @@ We are thrilled to introduce Seed-Coder, a powerful, transparent, and parameter-
   <img width="100%" src="imgs/seed-coder_intro_performance.jpg">
 </p>
-This repo contains the **Seed-Coder-8B-Base** model, which has the following features:
 - Type: Causal language models
 - Training Stage: Pretraining
 - Data Source: GitHub data, code-related web data
@@ -112,19 +112,14 @@ print(output[0]["generated_text"])
 ## Evaluation
-Seed-Coder-8B-Base has been evaluated across a variety of code understanding and generation benchmarks.
-It demonstrates strong capabilities in:
-- Fluent and contextually appropriate code completion.
-- Reasoning about code structure and inferring missing logic.
-- Generalizing across different programming languages, coding styles, and codebases.
 |            | 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).

   <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
 ## Evaluation
+Seed-Coder-8B-Base has been evaluated across a variety of code generation, code completion, and code reasoning benchmarks, achieving state-of-the-art performance among open-source models of similar size.
 |            | 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).