---
title: Qwen3.5-0.8B-int4-AutoRound
canonical_url: "https://www.modelscope.ai/models/Intel/Qwen3.5-0.8B-int4-AutoRound"
md_url: "https://www.modelscope.ai/models/Intel/Qwen3.5-0.8B-int4-AutoRound.md"
repository: Intel/Qwen3.5-0.8B-int4-AutoRound
last_updated: 2026-03-06
model_type:
  - qwen3_5
architectures:
  - Qwen3_5ForConditionalGeneration
base_model:
  - Qwen/Qwen3.5-0.8B
base_model_relation: quantized
parameters: 422.5M
tensor_type:
  - F16
  - I32
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 162
stars: 0
---

# Qwen3.5-0.8B-int4-AutoRound

> Qwen3.5-0.8B-int4-AutoRound - An open-source model by Intel on ModelScope. This model is an int4 model with groupsize 128 and symmetric quantization of Qwen/Qwen3.5-0.8B generated by intel/auto-round. Please follow the license of the original model.

Intel/Qwen3.5-0.8B-int4-AutoRound is a 422.5M-parameter machine learning model on ModelScope. derived from Qwen/Qwen3.5-0.8B.

- **Repository**: Intel/Qwen3.5-0.8B-int4-AutoRound
- **Parameters**: 422.5M
- **Base model**: Qwen/Qwen3.5-0.8B
- **Downloads**: 162
- **Stars**: 0
- **Last updated**: 2026-03-06

Source: https://www.modelscope.ai/models/Intel/Qwen3.5-0.8B-int4-AutoRound

---

## Model Details

This model is an int4 model with group_size 128 and symmetric quantization of [Qwen/Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B) generated by [intel/auto-round](https://github.com/intel/auto-round). Please follow the license of the original model.

## How to Use

### Environment

```bash
uv pip install vllm --torch-backend=auto --extra-index-url https://wheels.vllm.ai/nightly
uv pip install -U transformers
```

### VLLM Usage

```bash
vllm serve Intel/Qwen3.5-0.8B-int4-AutoRound \
    --host localhost \
    --trust-remote-code \
    --dtype bfloat16
```

## Generate the Model

```bash
auto-round --model_name Qwen/Qwen3.5-0.8B --bits 4 --iters 200 --output_dir Qwen3.5-0.8B-int4-AutoRound
```

## Ethical Considerations and Limitations

The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
Therefore, before deploying any applications of the model, developers should perform safety testing.

## Caveats and Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
Here are a couple of useful links to learn more about Intel's AI software:

- [Intel Neural Compressor](https://github.com/intel/neural-compressor)
- [AutoRound](https://github.com/intel/auto-round)

## Disclaimer

The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.

## Cite

```
@article{cheng2023optimize,
  title={Optimize weight rounding via signed gradient descent for the quantization of llms},
  author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi},
  journal={arXiv preprint arXiv:2309.05516},
  year={2023}
}
```

[arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)