Instructions to use mssfj/Qwen3.5-9B-GPTQ-INT4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mssfj/Qwen3.5-9B-GPTQ-INT4 with Transformers:

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

pipe = pipeline("text-generation", model="mssfj/Qwen3.5-9B-GPTQ-INT4")
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("mssfj/Qwen3.5-9B-GPTQ-INT4")
model = AutoModelForCausalLM.from_pretrained("mssfj/Qwen3.5-9B-GPTQ-INT4", 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 mssfj/Qwen3.5-9B-GPTQ-INT4 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-INT4"
# 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-INT4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mssfj/Qwen3.5-9B-GPTQ-INT4

SGLang

How to use mssfj/Qwen3.5-9B-GPTQ-INT4 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-INT4" \
    --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-INT4",
		"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-INT4" \
        --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-INT4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mssfj/Qwen3.5-9B-GPTQ-INT4 with Docker Model Runner:
```
docker model run hf.co/mssfj/Qwen3.5-9B-GPTQ-INT4
```

Qwen3.5-9B-GPTQ-INT4

This model is a GPTQ-quantized version of Qwen/Qwen3.5-9B.

Quantization

Method: GPTQ
Bits: 4
Group size: 128
desc_act: False
damp_percent: 0.1
Calibration preset: math_qa_cot
Calibration dataset: zwhe99/DeepMath-103K split train
Max calibration samples: 128
Max sequence length: 16384

Intended Use

This checkpoint was created to measure whether quantization degrades math reasoning quality, especially chain-of-thought stability.

Reproduction

This model was made by scripts in https://github.com/mssfj/lowbit-math-reasoning.git, execute command is following.

uv run python scripts/quantize_qwen35_9b_gptq.py \
  --model-name Qwen/Qwen3.5-9B \
  --output-dir /workspace/lowbit-math-reasoning/model/Qwen3.5-9B-GPTQ-INT4 \
  --dataset-name zwhe99/DeepMath-103K \
  --dataset-config '' \
  --dataset-split train \
  --calibration-preset math_qa_cot \
  --question-column question \
  --answer-column r1_solution_1 \
  --text-column r1_solution_1 \
  --max-calibration-samples 128 \
  --max-seq-len 16384 \
  --bits 4 \
  --group-size 128 \
  --damp-percent 0.1

Loading

from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "mssfj/Qwen3.5-9B-GPTQ-INT4"
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    trust_remote_code=True,
)

Notes

This repository contains quantized weights only.
Use a recent transformers build that supports Qwen3.5 GPTQ checkpoints.
Evaluation should be performed on math benchmarks such as GSM8K or MATH-500 to check answer accuracy and CoT-format failures.
Long math CoT calibration samples are often truncated heavily below --max-seq-len 8192.
Stop vLLM or other GPU-heavy processes before quantization to avoid OOM during GPTQ.

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Safetensors

Model size

9B params

Tensor type

I32

BF16

Model tree for mssfj/Qwen3.5-9B-GPTQ-INT4

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Qwen/Qwen3.5-9B-Base

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