---
title: Qwen3.8-Flash-Next-W4A16-AutoRound
canonical_url: "https://www.modelscope.cn/models/Intel/Qwen3.8-Flash-Next-W4A16-AutoRound"
md_url: "https://www.modelscope.cn/models/Intel/Qwen3.8-Flash-Next-W4A16-AutoRound.md"
repository: Intel/Qwen3.8-Flash-Next-W4A16-AutoRound
last_updated: 2026-09-19
license: other
pipeline_tag: image-text-to-text
tasks:
  - image-text-to-text
model_type:
  - qwen4_exp
architectures:
  - Qwen4ExpForConditionalGeneration
base_model:
  - Qwen/Qwen3.8-Flash-Next
base_model_relation: quantized
parameters: 75.4B
tensor_type:
  - I64
  - BF16
  - F16
  - I32
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 17623
stars: 0
---

# Qwen3.8-Flash-Next-W4A16-AutoRound

> Qwen3.8-Flash-Next-W4A16-AutoRound - Intel 在 ModelScope 开源的模型。This model is an INT4 model of Qwen3.8-Flash-Next generated by intel/auto-round with Tuning mode. Please follow the license of the original model.

Intel/Qwen3.8-Flash-Next-W4A16-AutoRound 是 ModelScope 魔搭社区上的 75.4B 参数image-text-to-text模型，采用 other 许可，基于 Qwen/Qwen3.8-Flash-Next 构建。

- **Repository**: Intel/Qwen3.8-Flash-Next-W4A16-AutoRound
- **License**: other
- **Tasks**: image-text-to-text
- **Parameters**: 75.4B
- **Base model**: Qwen/Qwen3.8-Flash-Next
- **Downloads**: 17623
- **Stars**: 0
- **Last updated**: 2026-09-19

Source: https://www.modelscope.cn/models/Intel/Qwen3.8-Flash-Next-W4A16-AutoRound

---

This model is an INT4 model of [Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next) generated by [intel/auto-round](https://github.com/intel/auto-round) with Tuning mode. Please follow the license of the original model.

| Configuration | GSM8K | MMLU | PIQA | HelleSwag | Average | Relative to BF16 |
|--------------|-------|----------|----------|----------|----------|------------------|
| BF16 | 0.9673 | 0.8651 | 0.8193 | 0.6927 | 0.8362 | - |
| INT4| 0.9682 | 0.8560 | 0.8215| 0.6873| 0.8332 | 99.64% |



## Generate the Model

~~~bash
auto-round \
  --model_name Qwen/Qwen3.8-Flash-Next \
  --scheme W4A16 \
  --iters 200 \
  --ignore_layers \
lm_head,embed_tokens,visual,linear_attn,self_attn,hyper_connection,mlp.gate,shared_expert,in_proj_a,in_proj_b,ple,mtp,indexer \
  --device_map auto \
  --format auto_round \
  --output_dir Qwen3.8-Flash-Next-W4A16-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)

## 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)