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)

# 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")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

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 Apr 30, 2025

Commit

f80ca53

verified ·

1 Parent(s): 14dbba3

Update README.md

Browse files

Files changed (1) hide show

README.md +10 -8

README.md CHANGED Viewed

@@ -70,14 +70,16 @@ Seed-Coder-8B-Instruct demonstrates strong performance across a variety of codin
 |             Model             | HumanEval | MBPP | MHPP | BigCodeBench (Full) | BigCodeBench (Hard) | LiveCodeBench (2410-2502) |
 |:-----------------------------:|:---------:|:----:|:----:|:-------------------:|:-------------------:|:-------------------------:|
-| CodeLlama-7B-Instruct         |    40.9   | 54.0 |  6.7 |         21.9        |         3.4         |            3.6            |
-| DeepSeek-Coder-6.7B-Instruct  |    74.4   | 74.9 | 20.0 |         35.5        |         10.1        |            9.6            |
-| CodeQwen1.5-7B-Chat           |    83.5   | 77.7 | 17.6 |         39.6        |         18.9        |            3.0            |
-| Yi-Coder-9B-Chat              |    82.3   | 82.0 | 26.7 |         38.1        |         11.5        |            17.5           |
-| Llama-3.1-8B-Instruct         |    68.3   | 70.1 | 17.1 |         36.6        |         13.5        |            11.5           |
-| OpenCoder-8B-Instruct         |    83.5   | 79.1 | 30.5 |         40.3        |         16.9        |            17.1           |
-| Qwen2.5-Coder-7B-Instruct     |    88.4   | 82.0 | 26.7 |         41.0        |         18.2        |            17.3           |
-| Seed-Coder-8B-Instruct        |    84.8   | 85.2 | 36.2 |         53.3        |         20.5        |            24.7           |
 For detailed results, please check our [📑 paper](https://arxiv.org/pdf/xxx.xxxxx).

 |             Model             | HumanEval | MBPP | MHPP | BigCodeBench (Full) | BigCodeBench (Hard) | LiveCodeBench (2410-2502) |
 |:-----------------------------:|:---------:|:----:|:----:|:-------------------:|:-------------------:|:-------------------------:|
+|     CodeLlama-7B-Instruct     |    40.9   | 54.0 |  6.7 |         21.9        |         3.4         |            3.6            |
+|  DeepSeek-Coder-6.7B-Instruct |    74.4   | 74.9 | 20.0 |         35.5        |         10.1        |            9.6            |
+|      CodeQwen1.5-7B-Chat      |    83.5   | 77.7 | 17.6 |         39.6        |         18.9        |            3.0            |
+|        Yi-Coder-9B-Chat       |    82.3   | 82.0 | 26.7 |         38.1        |         11.5        |            17.5           |
+|     Llama-3.1-8B-Instruct     |    68.3   | 70.1 | 17.1 |         36.6        |         13.5        |            11.5           |
+|     OpenCoder-8B-Instruct     |    83.5   | 79.1 | 30.5 |         40.3        |         16.9        |            17.1           |
+|   Qwen2.5-Coder-7B-Instruct   |    88.4   | 82.0 | 26.7 |         41.0        |         18.2        |            17.3           |
+|            Qwen3-8B           |    84.8   | 77.0 | 32.8 |         51.7        |         23.0        |            23.5           |
+| Seed-Coder-8B-Instruct (0411) |    84.8   | 85.2 | 36.2 |         53.3        |         20.5        |            24.7           |
 For detailed results, please check our [📑 paper](https://arxiv.org/pdf/xxx.xxxxx).