Instructions to use AxisQuant/Qwen3.5-4B-INT8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AxisQuant/Qwen3.5-4B-INT8 with Transformers:

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

pipe = pipeline("text-generation", model="AxisQuant/Qwen3.5-4B-INT8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AxisQuant/Qwen3.5-4B-INT8")
model = AutoModelForCausalLM.from_pretrained("AxisQuant/Qwen3.5-4B-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 AxisQuant/Qwen3.5-4B-INT8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AxisQuant/Qwen3.5-4B-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": "AxisQuant/Qwen3.5-4B-INT8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AxisQuant/Qwen3.5-4B-INT8

SGLang

How to use AxisQuant/Qwen3.5-4B-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 "AxisQuant/Qwen3.5-4B-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": "AxisQuant/Qwen3.5-4B-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 "AxisQuant/Qwen3.5-4B-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": "AxisQuant/Qwen3.5-4B-INT8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AxisQuant/Qwen3.5-4B-INT8 with Docker Model Runner:
```
docker model run hf.co/AxisQuant/Qwen3.5-4B-INT8
```

Qwen3.5-4B-INT8 / README.md

prashantcp8

Update README.md

74c2ca3 verified about 2 months ago

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	---
	license: apache-2.0
	base_model: Qwen/Qwen3.5-4B
	tags:
	- quantization
	- int8
	- bitsandbytes
	- qwen
	- qwen3.5
	language:
	- en
	pipeline_tag: text-generation
	library_name: transformers
	---

	# Qwen3.5-4B-INT8

	Author: Prashant Takale

	## Model Description

	This is an INT8 quantized version of [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) using bitsandbytes LLM.int8() quantization.

	### What I Did

	- Quantized the original Qwen3.5-4B model from BF16 to INT8 precision using bitsandbytes
	- Reduced memory footprint by 42.4% while maintaining model quality
	- Benchmarked on WikiText-2 (perplexity) and GSM8K (math reasoning)

	### Why I Did This

	1. Memory Efficiency: Reduce GPU memory requirements to run on smaller GPUs
	2. Faster Inference: INT8 operations can be faster on compatible hardware
	3. Accessibility: Enable deployment on consumer-grade hardware with limited VRAM

	## Benchmark Results

	\| Metric \| Baseline (BF16) \| INT8 \| Change \|
	\|--------\|-----------------\|------\|--------\|
	\| Memory \| 8.41 GB \| 4.84 GB \| -42.4% \|
	\| Perplexity (WikiText-2) \| 12.59 \| 12.75 \| +1.30% \|
	\| GSM8K Accuracy \| 86.00% \| ~86% \| Minimal degradation \|

	### Key Findings

	- 1.74x memory reduction with minimal quality loss
	- Only +1.30% perplexity increase on WikiText-2
	- Math reasoning capabilities preserved on GSM8K benchmark

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained(
	"AxisQuant/Qwen3.5-4B-INT8",
	device_map="auto"
	)
	tokenizer = AutoTokenizer.from_pretrained("prashantcp8/Qwen3.5-4B-INT8")

	messages = [{"role": "user", "content": "What is machine learning?"}]
	prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

	outputs = model.generate(**inputs, max_new_tokens=256)
	response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
	print(response)
	```

	## Technical Details

	- Quantization Method: bitsandbytes LLM.int8()
	- Base Model: Qwen/Qwen3.5-4B
	- Original Precision: BF16
	- Quantized Precision: INT8
	- Framework: Transformers + bitsandbytes


	## Limitations

	- Requires `bitsandbytes` library for optimal performance
	- Some operations may cast to FP16 during inference
	- Best suited for NVIDIA GPUs with INT8 tensor core support

	## Citation

	If you use this model, please cite the original Qwen3.5 model:

	```bibtex
	@misc{qwen3.5,
	title={Qwen3.5 Technical Report},
	author={Qwen Team},
	year={2025},
	publisher={Alibaba}
	}
	```

	## Author

	Prashant Takale

	---

	> Note: This model is for testing and experimental purposes only. There are still some improvements required in terms of inference speed and accuracy. I plan to explore better quantization methods like AWQ or GPTQ in future iterations for optimized performance.