README.md

---
pipeline_tag: image-text-to-text
license: other
license_name: minimax-community
license_link: LICENSE
library_name: transformers
tags:
  - multimodal
  - moe
  - agent
  - coding
  - video
---

This model is a MXFP4 mixed model of [MiniMax-M3](https://huggingface.co/MiniMaxAI/MiniMax-M3) generated by [intel/auto-round](https://github.com/intel/auto-round) with llm_compressor format. Please follow the license of the original model.
- MOE (block_sparse_moe.experts): MXFP4
- MOE (block_sparse_moe.shared_experts): MXFP8

| Configuration | GSM8K | MMLU | PIQA | HelleSwag | Average | Relative to BF16 |
|--------------|-------|----------|----------|----------|----------|------------------|
| BF16 | 95.30 | 0.8415 | 0.8259 | 0.6714 | 0.82295 | - |
| MXFP4 (experts) + MXFP8 (shared_experts) | 95.30 | 0.8282 | 0.8237 | 0.6528 | 0.814425 | 98.96% |


## How to Run Locally


```
vllm serve \
  INCModelSharing/MiniMax-M3-MXFP4-Mixed-CT-AutoRound \
  --tensor-parallel-size 2 \
  --max-model-len 131072 \
  --tool-call-parser minimax_m3 \
  --reasoning-parser minimax_m3 \
  --enable-auto-tool-choice \
  --port 8000
````

```
curl -s http://127.0.0.1:8000/v1/chat/completions   -H "Content-Type: application/json"   -d '{
    "model": "INCModelSharing/MiniMax-M3-MXFP4-Mixed-CT-AutoRound",
    "messages": [
      {"role":"user","content":"2+3=?"}
    ],
    "max_tokens": 10,
    "extra_body": {
      "chat_template_kwargs": {
        "enable_thinking": true
      }
    }
  }' | python3 -m json.tool
```



## Generate the Model


~~~bash
auto-round MiniMaxAI/MiniMax-M3 --model_free \
  --scheme MXFP8 \
  --ignore_layers vision_tower,lm_head,block_sparse_moe.gate,embed_tokens,self_attn,\
patch_merge_mlp,multi_modal_projector,mlp.gate_proj,mlp.up_proj,mlp.down_proj \
  --layer_config "{block_sparse_moe.experts:{bits:4,data_type:mx_fp}}" \
  --format llm_compressor \
  --output_dir "./MiniMax-M3-MXFP4-Mixed-CT-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)