---
title: GLM-5-int4-mixed-AutoRound
canonical_url: "https://www.modelscope.cn/models/INC4AI/GLM-5-int4-mixed-AutoRound"
md_url: "https://www.modelscope.cn/models/INC4AI/GLM-5-int4-mixed-AutoRound.md"
repository: INC4AI/GLM-5-int4-mixed-AutoRound
last_updated: 2026-02-25
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - glm_moe_dsa
architectures:
  - GlmMoeDsaForCausalLM
base_model:
  - zai-org/GLM-5
base_model_relation: quantized
parameters: 104.0B
tensor_type:
  - I32
  - F16
  - BF16
  - F32
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 26
stars: 0
---

# GLM-5-int4-mixed-AutoRound

> GLM-5-int4-mixed-AutoRound - INC4AI 在 ModelScope 开源的模型。This model is a mixed int4 model with groupsize 128 and symmetric quantization of zai-org/GLM-5 generated by intel/auto-round. Please follow the license of the original model.

INC4AI/GLM-5-int4-mixed-AutoRound 是 ModelScope 魔搭社区上的 104.0B 参数text-generation模型，基于 zai-org/GLM-5 构建。

- **Repository**: INC4AI/GLM-5-int4-mixed-AutoRound
- **Tasks**: text-generation
- **Parameters**: 104.0B
- **Base model**: zai-org/GLM-5
- **Downloads**: 26
- **Stars**: 0
- **Last updated**: 2026-02-25

Source: https://www.modelscope.cn/models/INC4AI/GLM-5-int4-mixed-AutoRound

---

## Model Details

This model is a mixed int4 model with group_size 128 and symmetric quantization of [zai-org/GLM-5](https://huggingface.co/zai-org/GLM-5) generated by [intel/auto-round](https://github.com/intel/auto-round). Please follow the license of the original model.

## Generate the Model

```bash
auto-round --model_name zai-org/GLM-5 --scheme w4a16_mixed --iters 0 --output_dir GLM-5-int4-mixed-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)