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

Libraries

How to use unsloth/MiniMax-M3 with Transformers:

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

pipe = pipeline("image-text-to-text", model="unsloth/MiniMax-M3", 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("unsloth/MiniMax-M3", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("unsloth/MiniMax-M3", 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?"}
        ]
    },
]
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 unsloth/MiniMax-M3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/MiniMax-M3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/MiniMax-M3",
		"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/unsloth/MiniMax-M3

SGLang

How to use unsloth/MiniMax-M3 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 "unsloth/MiniMax-M3" \
    --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": "unsloth/MiniMax-M3",
		"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 "unsloth/MiniMax-M3" \
        --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": "unsloth/MiniMax-M3",
		"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 unsloth/MiniMax-M3 with Docker Model Runner:
```
docker model run hf.co/unsloth/MiniMax-M3
```

MiniMax-M3 / README.md

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Update README.md

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

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

	<div align="center" style="line-height: 1.4; font-size:16px; margin-top: 30px;">
	Join Our
	<a href="https://platform.minimaxi.com/docs/faq/contact-us" target="_blank" style="font-size:17px; margin: 2px;">
	💬 WeChat
	</a> \|
	<a href="https://discord.com/invite/DPC4AHFCBw" target="_blank" style="font-size:17px; margin: 2px;">
	🧩 Discord
	</a>
	community.
	</div>
	<div align="center" style="line-height: 1.2; font-size:16px;">
	<a href="https://agent.minimax.io/" target="_blank" style="display: inline-block; margin: 4px;">
	MiniMax Agent
	</a> \|
	<a href="https://platform.minimax.io/docs/guides/text-generation" target="_blank" style="display: inline-block; margin: 4px;">
	⚡️ API
	</a> \|
	<a href="https://github.com/MiniMax-AI/cli" style="display: inline-block; margin: 4px;">
	CLI
	</a> \|
	<a href="https://www.minimax.io" target="_blank" style="display: inline-block; margin: 4px;">
	MiniMax Website
	</a>
	</div>
	<div align="center" style="line-height: 1.2; font-size:16px; margin-bottom: 30px;">
	<a href="https://huggingface.co/MiniMaxAI" target="_blank" style="margin: 2px;">
	🤗 Hugging Face
	</a> \|
	<a href="https://github.com/MiniMax-AI/MiniMax-M3" target="_blank" style="margin: 2px;">
	🐙 GitHub
	</a> \|
	<a href="https://www.modelscope.cn/organization/MiniMax" target="_blank" style="margin: 2px;">
	🤖️ ModelScope
	</a> \|
	<a href="https://huggingface.co/MiniMaxAI/MiniMax-M3/blob/main/LICENSE" style="margin: 2px;">
	📄 LICENSE
	</a>
	</div>

	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.

	## Model Details
	\| \| \|
	\| --- \| --- \|
	\| Architecture \| MoE + MSA (MiniMax Sparse Attention) \|
	\| Total Parameters \| ~428B \|
	\| Activated Parameters \| ~23B \|
	\| Experts \| 128 (4 active per token) \|
	\| Layers \| 60 \|
	\| Context Length \| 1M tokens \|
	\| Modalities \| Text, Image, Video \|
	\| Precision \| bfloat16 \|
	\| Transformers \| ≥ 4.52.4 (`trust_remote_code=True`) \|
	\| License \| [MiniMax Community License](LICENSE) \|
	<p align="center">
	<img width="100%" src="figures/benchmark.jpeg">
	</p>

	## 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

	We recommend using [SGLang](https://docs.sglang.io/) to serve MiniMax-M3. Please refer to our [SGLang Deployment Guide](./docs/sglang_deploy_guide.md).

	### vLLM

	We recommend using [vLLM](https://github.com/vllm-project/vllm) to serve MiniMax-M3. Please refer to our [vLLM Deployment Guide](./docs/vllm_deploy_guide.md).

	### Transformers

	We recommend using [Transformers](https://github.com/huggingface/transformers) to serve MiniMax-M3. Please refer to our [Transformers Deployment Guide](./docs/transformers_deploy_guide.md).

	### ModelScope

	You can also get model weights from [ModelScope](https://modelscope.cn/models/MiniMax/MiniMax-M3).

	### Inference Parameters

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

	```
	You are a helpful assistant. Your name is MiniMax-M3 and was built by MiniMax.
	```

	## Tool Calling Guide

	Please refer to our [Tool Calling Guide](./docs/tool_calling_guide.md).

	## Contact Us

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