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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("willyli/Seed-Coder-8B-Instruct-KTO")
model = AutoModelForCausalLM.from_pretrained("willyli/Seed-Coder-8B-Instruct-KTO")
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 willyli/Seed-Coder-8B-Instruct-KTO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "willyli/Seed-Coder-8B-Instruct-KTO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "willyli/Seed-Coder-8B-Instruct-KTO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use willyli/Seed-Coder-8B-Instruct-KTO 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 "willyli/Seed-Coder-8B-Instruct-KTO" \
    --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": "willyli/Seed-Coder-8B-Instruct-KTO",
		"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 "willyli/Seed-Coder-8B-Instruct-KTO" \
        --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": "willyli/Seed-Coder-8B-Instruct-KTO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Seed-Coder-8B-Instruct-KTO / last-checkpoint /chat_template.jinja

willyli

Training in progress, step 100, checkpoint

e9e95f0 verified 12 months ago

raw

history blame contribute delete

610 Bytes

	{% set has_system = messages[0]['role'] == 'system' %}{% if not has_system %}{{ bos_token + 'system
	You are an AI programming assistant, utilizing the Seed-Coder model, developed by ByteDance Seed, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer.

	' + eos_token }}{% endif %}{% for message in messages %}{{ bos_token + message['role'] + '
	' + message['content'] \| trim + eos_token }}{% endfor %}{% if add_generation_prompt %}{{ bos_token + 'assistant
	'}}{% endif %}