Instructions to use KSKOOO/HakuChat-Code-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KSKOOO/HakuChat-Code-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="KSKOOO/HakuChat-Code-9B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("KSKOOO/HakuChat-Code-9B")
model = AutoModelForMultimodalLM.from_pretrained("KSKOOO/HakuChat-Code-9B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KSKOOO/HakuChat-Code-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KSKOOO/HakuChat-Code-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KSKOOO/HakuChat-Code-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/KSKOOO/HakuChat-Code-9B

SGLang

How to use KSKOOO/HakuChat-Code-9B 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 "KSKOOO/HakuChat-Code-9B" \
    --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": "KSKOOO/HakuChat-Code-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "KSKOOO/HakuChat-Code-9B" \
        --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": "KSKOOO/HakuChat-Code-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use KSKOOO/HakuChat-Code-9B with Docker Model Runner:
```
docker model run hf.co/KSKOOO/HakuChat-Code-9B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

HakuChat-Code-9B

HakuChat 是一个本地部署的中文代码助手，本仓库为 9B 参数全量权重（合并后，未量化）。

能力

Python 代码生成、补全、函数实现与解释
工具调用（calculator / web.search 轨迹）
长上下文：原生支持 256k，部署配置 128k
中文对话与身份统一为 HakuChat

评测结果（线上 int4 / 128k，temperature=0）

Benchmark	分数	样本数
MMLU	73.1	171
C-Eval	78.85	104
GSM8K	86.67	60
HumanEval (pass@1)	63.33	60

口径说明：0-shot 生成式采样，采样数见上表；同类模型多为厂商 5-shot/bf16 成绩，数值仅供量级参考。

使用

from transformers import AutoModelForImageTextToText, AutoTokenizer

tok = AutoTokenizer.from_pretrained("KSKOOO/HakuChat-Code-9B")
model = AutoModelForImageTextToText.from_pretrained(
    "KSKOOO/HakuChat-Code-9B", dtype="auto", device_map="auto")

对话模板内置身份设定，调用时无需额外 system prompt。

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Safetensors

Model size

9B params

Tensor type

BF16

Model tree for KSKOOO/HakuChat-Code-9B

Quantizations

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