Instructions to use FenomAI/MiniMax-M3-AWQ-INT4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FenomAI/MiniMax-M3-AWQ-INT4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="FenomAI/MiniMax-M3-AWQ-INT4", 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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("FenomAI/MiniMax-M3-AWQ-INT4", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("FenomAI/MiniMax-M3-AWQ-INT4", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FenomAI/MiniMax-M3-AWQ-INT4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FenomAI/MiniMax-M3-AWQ-INT4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FenomAI/MiniMax-M3-AWQ-INT4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/FenomAI/MiniMax-M3-AWQ-INT4

SGLang

How to use FenomAI/MiniMax-M3-AWQ-INT4 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 "FenomAI/MiniMax-M3-AWQ-INT4" \
    --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": "FenomAI/MiniMax-M3-AWQ-INT4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "FenomAI/MiniMax-M3-AWQ-INT4" \
        --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": "FenomAI/MiniMax-M3-AWQ-INT4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use FenomAI/MiniMax-M3-AWQ-INT4 with Docker Model Runner:
```
docker model run hf.co/FenomAI/MiniMax-M3-AWQ-INT4
```

MiniMax-M3-AWQ-INT4 / README.md

FenomAI

Duplicate from cyankiwi/MiniMax-M3-AWQ-INT4

8c36e64 about 1 month ago

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	---
	base_model: MiniMaxAI/MiniMax-M3
	library_name: transformers
	license: other
	license_link: LICENSE
	license_name: minimax-community
	pipeline_tag: image-text-to-text
	tags:
	- multimodal
	- moe
	- agent
	- coding
	- video
	---

	<div align="center">
	<img src="https://huggingface.co/buckets/cyankiwi/activation-aware-2.0/resolve/banner/cyankiwi-banner-awq-0.png">
	</div>

	<div align="left">
	<table align="center" style="border-collapse:collapse; border:none;">
	<tr style="border:none;">
	<td align="right" style="border:none; padding:4px 12px 4px 0;"><b>Version</b></td>
	<td align="left" style="border:none; padding:4px 0;">26.05.01</td>
	</tr>
	<tr style="border:none;">
	<td align="right" style="border:none; padding:4px 12px 4px 0;"><b>Calibration</b></td>
	<td align="left" style="border:none; padding:4px 0;">
	<a href="https://huggingface.co/datasets/cyankiwi/calibration" target="_blank">STEM and Agentic</a>
	</td>
	</tr>
	<tr style="border:none;">
	<td align="right" style="border:none; padding:4px 12px 4px 0;"><b>Languages</b></td>
	<td align="left" style="border:none; padding:4px 0;">
	<code>EN</code> <code>ZH</code> <code>HI</code> <code>AR</code> <code>RU</code>
	<code>JA</code> <code>KO</code> <code>NL</code> <code>FR</code> <code>ES</code>
	</td>
	</tr>
	<tr style="border:none;">
	<td align="right" style="border:none; padding:4px 12px 4px 0;"><b>Model Size</b></td>
	<td align="left" style="border:none; padding:4px 0;">240.30 GB</td>
	</tr>
	<tr style="border:none;">
	<td align="right" style="border:none; padding:4px 12px 4px 0;"><b>Contact</b></td>
	<td align="left" style="border:none; padding:4px 0;">
	<a href="mailto:ton@cyan.kiwi">Email</a>
	</td>
	</tr>
	</table>
	</div>

	## Serving with vLLM

	This checkpoint needs a patched vLLM (MiniMax-M3 compressed-tensors support).
	The patch is Python-only, so it installs on top of upstream's **precompiled
	binaries** — no CUDA compilation.

	### Install

	```bash
	# uv (skip if already installed)
	curl -LsSf https://astral.sh/uv/install.sh \| sh

	# clone the fork + fetch the upstream base commit
	git clone https://github.com/toncao/vllm.git
	cd vllm
	git remote add upstream https://github.com/vllm-project/vllm.git
	git fetch upstream a7fdfeef72323eb3db6f0620e4ea200290d0ca5a
	git checkout minimax-m3-compressed-tensors

	# Python 3.12 env + install with upstream precompiled kernels
	uv venv --python 3.12
	source .venv/bin/activate
	VLLM_USE_PRECOMPILED=1 uv pip install -e . --torch-backend=auto
	```

	### Serve

	```bash
	vllm serve cyankiwi/MiniMax-M3-AWQ-INT4 --block-size 128
	```

	---

	<div align="center">
	<img width="60%" src="figures/logo.svg" alt="MiniMax">
	</div>
	<hr>

	<p align="center">
	<a href="https://agent.minimax.io/" target="_blank"><img src="https://img.shields.io/badge/MiniMax%20Agent-FF6C37?style=for-the-badge&logo=minimax&logoColor=white" alt="MiniMax Agent"></a>
	<a href="https://platform.minimax.io/docs/guides/text-generation" target="_blank"><img src="https://img.shields.io/badge/API-FF6C37?style=for-the-badge&logo=minimax&logoColor=white" alt="API"></a>
	<a href="https://www.minimax.io" target="_blank"><img src="https://img.shields.io/badge/MiniMax%20Website-FF6C37?style=for-the-badge&logo=minimax&logoColor=white" alt="MiniMax Website"></a>
	<br>
	<a href="https://modelscope.cn/organization/minimax" target="_blank" rel="noopener noreferrer"><img alt="ModelScope MiniMax AI" src="https://img.shields.io/badge/ModelScope-MiniMax%20AI-white?labelColor=%23EF3D5D"/></a>
	<a href="https://platform.minimaxi.com/docs/faq/contact-us" target="_blank"><img src="https://img.shields.io/badge/WeChat-07C160?style=for-the-badge&logo=wechat&logoColor=white" alt="WeChat"></a>
	<a href="https://discord.com/invite/DPC4AHFCBw" target="_blank"><img src="https://img.shields.io/badge/Discord-5865F2?style=for-the-badge&logo=discord&logoColor=white" alt="Discord"></a>
	<a href="https://huggingface.co/MiniMaxAI" target="_blank"><img src="https://img.shields.io/badge/Hugging%20Face-FFD21E?style=for-the-badge&logo=huggingface&logoColor=black" alt="Hugging Face"></a>
	<a href="https://github.com/MiniMax-AI/MiniMax-M3" target="_blank"><img src="https://img.shields.io/badge/GitHub-181717?style=for-the-badge&logo=github&logoColor=white" alt="GitHub"></a>
	<a href="https://arxiv.org/abs/2606.13392" target="_blank"><img src="https://img.shields.io/badge/arXiv-2606.13392-B31B1B?style=for-the-badge&logo=arxiv&logoColor=white" alt="arXiv Paper"></a>
	<a href="https://huggingface.co/MiniMaxAI/MiniMax-M3/blob/main/LICENSE" target="_blank"><img src="https://img.shields.io/badge/LICENSE-4CAF50?style=for-the-badge&logo=creativecommons&logoColor=white" alt="LICENSE"></a>
	</p>

	MiniMax-M3 is a native multimodal model with 1M context. It has ~428B parameters and ~23B activated parameters.

	Highlights:
	- Native Multimodality: M3 undergoes mixed-modality training from the very first step, enabling deeper semantic fusion across text, image, and video.
	- Context Scaling via Sparse Attention: M3 introduces MiniMax Sparse Attention (MSA) to improve long context efficiency. M3 delivers 9× prefill and 15× decode speedups compared to M2 at 1M context, reducing per-token compute to 1/20.
	- Coding & Cowork Capability: M3 achieves frontier-level performance across long-horizon agentic benchmarks, excelling in both coding and cowork.


	<p align="center">
	<img width="100%" src="figures/benchmark.jpeg">
	</p>

	## MiniMax Sparse Attention (MSA)

	M3 is powered by [MiniMax Sparse Attention (MSA)](https://github.com/MiniMax-AI/MSA), a high-performance sparse attention operator designed for million-token contexts. Compared with GQA, MSA dramatically reduces the attention compute and memory footprint while preserving model quality.

	<p align="center">
	<img width="100%" src="figures/efficiency_gqa_vs_msa.png" alt="GQA vs MSA Efficiency Comparison">
	</p>

	> 📄 Read the technical report: [arXiv:2606.13392](https://arxiv.org/abs/2606.13392) · [Hugging Face Papers](https://huggingface.co/papers/2606.13392)

	## How to Use

	- [MiniMax Agent](https://agent.minimax.io/)
	- [MiniMax API](https://platform.minimax.io/)

	M3 supports two reasoning modes:
	- thinking — for complex reasoning, agentic tasks, and long-horizon collaboration.
	- non-thinking — for latency-sensitive scenarios such as chat and code completion.

	## Local Deployment

	Download the model:

	```bash
	hf download MiniMaxAI/MiniMax-M3 --local-dir MiniMax-M3
	```

	We recommend the following inference frameworks (listed alphabetically) to serve the model:

	- [SGLang](https://docs.sglang.io/) - see [SGLang cookbook](https://docs.sglang.io/cookbook/autoregressive/MiniMax/MiniMax-M3).

	- [vLLM](https://github.com/vllm-project/vllm) - see [vLLM recipes](https://recipes.vllm.ai/MiniMaxAI/MiniMax-M3).

	- [Transformers](https://github.com/huggingface/transformers) - see [Transformers docs](https://huggingface.co/docs/transformers/model_doc/minimax_m3_vl).


	### Inference Parameters

	We recommend the following parameters for best performance: `temperature=1.0`, `top_p=0.95`, `top_k=40`.

	## Contact Us

	Contact us at [model@minimax.io](mailto:model@minimax.io).