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

Libraries

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

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

pipe = pipeline("text-generation", model="ByteDance-Seed/Seed-Coder-8B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ByteDance-Seed/Seed-Coder-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained("ByteDance-Seed/Seed-Coder-8B-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ByteDance-Seed/Seed-Coder-8B-Instruct 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-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ByteDance-Seed/Seed-Coder-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use ByteDance-Seed/Seed-Coder-8B-Instruct 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-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ByteDance-Seed/Seed-Coder-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ByteDance-Seed/Seed-Coder-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

yuyuzhang commited on May 9, 2025

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@@ -7,8 +7,19 @@ base_model:
 # 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.

 # Seed-Coder-8B-Instruct
 ## 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>
+## Highlight
 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 **large scale synthetic data**, emphasizing diversity, difficulty, scalability, and quality.
 - 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.