Instructions to use mssfj/Qwen3.5-9B-GPTQ-INT8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mssfj/Qwen3.5-9B-GPTQ-INT8 with Transformers:

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

pipe = pipeline("text-generation", model="mssfj/Qwen3.5-9B-GPTQ-INT8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mssfj/Qwen3.5-9B-GPTQ-INT8")
model = AutoModelForCausalLM.from_pretrained("mssfj/Qwen3.5-9B-GPTQ-INT8")
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 Settings

vLLM

How to use mssfj/Qwen3.5-9B-GPTQ-INT8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mssfj/Qwen3.5-9B-GPTQ-INT8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mssfj/Qwen3.5-9B-GPTQ-INT8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mssfj/Qwen3.5-9B-GPTQ-INT8

SGLang

How to use mssfj/Qwen3.5-9B-GPTQ-INT8 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 "mssfj/Qwen3.5-9B-GPTQ-INT8" \
    --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": "mssfj/Qwen3.5-9B-GPTQ-INT8",
		"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 "mssfj/Qwen3.5-9B-GPTQ-INT8" \
        --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": "mssfj/Qwen3.5-9B-GPTQ-INT8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mssfj/Qwen3.5-9B-GPTQ-INT8 with Docker Model Runner:
```
docker model run hf.co/mssfj/Qwen3.5-9B-GPTQ-INT8
```

quant script(quantize_qwen35_9b_gptq.py)

by sudalmk - opened Apr 16

Discussion

sudalmk

Apr 16

good, very good, can you please give me the quant script?

mssfj

Owner 19 days ago

Hi! Thank you for the kind words, and I'm truly sorry for the late reply—I didn't notice your comment until now!

The quantization script quantize_qwen35_9b_gptq.py is available in my GitHub repository.
You can find it under the /quantization directory here:
https://github.com/mssfj/lowbit-math-reasoning

Since it's licensed under the MIT License, feel free to use and modify it. Hope this helps, and thanks again for your feedback!

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