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
```

Qwen3.5-9B-GPTQ-INT8 / README.md

mssfj

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	---
	language:
	- en
	license: apache-2.0
	base_model: Qwen/Qwen3.5-9B
	tags:
	- qwen
	- gptq
	- quantized
	- math
	- causal-lm
	library_name: transformers
	pipeline_tag: text-generation
	---

	# Qwen3.5-9B-GPTQ-INT8

	This model is a GPTQ-quantized version of `Qwen/Qwen3.5-9B` with a normalized text-only `config.json`.

	## Quantization

	- Method: GPTQ
	- Bits: 8
	- 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

	## Reproduction

	```bash
	uv run python quantization/quantize_qwen35_9b_gptq.py \
	--model-name Qwen/Qwen3.5-9B \
	--output-dir /workspace/lowbit-math-reasoning/experiments/models/Qwen3.5-9B-GPTQ-INT8 \
	--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 8 \
	--group-size 128 \
	--damp-percent 0.1
	```

	The current quantization script rewrites `config.json` after `save_pretrained()` so the exported checkpoint uses the same text-only `qwen3_5_text` layout as the working INT4 checkpoint.

	## Validation

	This normalized-config checkpoint was re-evaluated on GSM8K and matched the original INT8 accuracy while improving throughput substantially.

	- Original INT8: EM 0.96, 105.98 tok/s
	- Fixed-config INT8: EM 0.96, 150.84 tok/s

	## Notes

	- This repository contains quantized weights only.
	- The checkpoint is intended for text-only evaluation.
	- `vLLM` loads this checkpoint as `gptq_marlin`.