Instructions to use intel-ai/MiniMax-M3-MXFP4-CT-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use intel-ai/MiniMax-M3-MXFP4-CT-AutoRound with Transformers:

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

pipe = pipeline("text-generation", model="intel-ai/MiniMax-M3-MXFP4-CT-AutoRound", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("intel-ai/MiniMax-M3-MXFP4-CT-AutoRound", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("intel-ai/MiniMax-M3-MXFP4-CT-AutoRound", trust_remote_code=True, device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use intel-ai/MiniMax-M3-MXFP4-CT-AutoRound with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "intel-ai/MiniMax-M3-MXFP4-CT-AutoRound"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "intel-ai/MiniMax-M3-MXFP4-CT-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/intel-ai/MiniMax-M3-MXFP4-CT-AutoRound

SGLang

How to use intel-ai/MiniMax-M3-MXFP4-CT-AutoRound 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 "intel-ai/MiniMax-M3-MXFP4-CT-AutoRound" \
    --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": "intel-ai/MiniMax-M3-MXFP4-CT-AutoRound",
		"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 "intel-ai/MiniMax-M3-MXFP4-CT-AutoRound" \
        --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": "intel-ai/MiniMax-M3-MXFP4-CT-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use intel-ai/MiniMax-M3-MXFP4-CT-AutoRound with Docker Model Runner:
```
docker model run hf.co/intel-ai/MiniMax-M3-MXFP4-CT-AutoRound
```

MiniMax-M3-MXFP4-CT-AutoRound / README.md

INC4AI

Update model card according to templates/model_card.md using INC-Quantization-Recipes

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---
base_model:
- MiniMaxAI/MiniMax-M3
license: other
pipeline_tag: text-generation
library_name: transformers
tags:
- auto-round
- mxfp8
- compressed-tensors
---
## Model Details

This model is an MXFP8 model of [MiniMaxAI/MiniMax-M3](https://huggingface.co/MiniMaxAI/MiniMax-M3) generated by [intel/auto-round](https://github.com/intel/auto-round) with 200 tuning iterations. Please follow the license of the original model.

- **Quantized (MXFP8)**: linear layers
- **Kept in BF16**: lm_head / embed_tokens

## How to Use

### Environment

```bash
pip install vllm==0.29.0
```

### vLLM Usage

```bash
vllm serve intel-ai/MiniMax-M3-MXFP4-CT-AutoRound \
    --trust-remote-code
```

## Evaluation

| Benchmark | BF16 | MXFP8 | Recovery |
|---|---:|---:|---:|
| GSM8K | 94.60 | 94.10 | 99.47% |
| MMLU | 86.20 | 85.80 | 99.54% |
| PIQA | 83.10 | 82.90 | 99.76% |
| HellaSwag | 75.40 | 74.80 | 99.20% |
| **Average** | **84.83** | **84.40** | **99.49%** |

## Generate the Model

```bash
auto-round \
    --model_name MiniMaxAI/MiniMax-M3 \
    --iters 200 \
    --scheme MXFP8 \
    --ignore_layers "lm_head,embed_tokens" \
    --format llm_compressor \
    --output_dir MiniMax-M3-MXFP4-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)
- [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)

Signed-off-by: Lv, Kaokao <kaokao.lv@intel.com>

	---
	base_model:
	- MiniMaxAI/MiniMax-M3
	license: other
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- auto-round
	- mxfp8
	- compressed-tensors
	---

	## Model Details

	This model is an MXFP8 model of [MiniMaxAI/MiniMax-M3](https://huggingface.co/MiniMaxAI/MiniMax-M3) generated by [intel/auto-round](https://github.com/intel/auto-round) with 200 tuning iterations. Please follow the license of the original model.

	- Quantized (MXFP8): linear layers
	- Kept in BF16: lm_head / embed_tokens

	## How to Use

	### Environment

	```bash
	pip install vllm==0.29.0
	```

	### vLLM Usage

	```bash
	vllm serve intel-ai/MiniMax-M3-MXFP4-CT-AutoRound \
	--trust-remote-code
	```

	## Evaluation

	\| Benchmark \| BF16 \| MXFP8 \| Recovery \|
	\|---\|---:\|---:\|---:\|
	\| GSM8K \| 94.60 \| 94.10 \| 99.47% \|
	\| MMLU \| 86.20 \| 85.80 \| 99.54% \|
	\| PIQA \| 83.10 \| 82.90 \| 99.76% \|
	\| HellaSwag \| 75.40 \| 74.80 \| 99.20% \|
	\| Average \| 84.83 \| 84.40 \| 99.49% \|

	## Generate the Model

	```bash
	auto-round \
	--model_name MiniMaxAI/MiniMax-M3 \
	--iters 200 \
	--scheme MXFP8 \
	--ignore_layers "lm_head,embed_tokens" \
	--format llm_compressor \
	--output_dir MiniMax-M3-MXFP4-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)
	- [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)

	Signed-off-by: Lv, Kaokao <kaokao.lv@intel.com>