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

Libraries

How to use MiniMaxAI/MiniMax-M3 with Transformers:

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

pipe = pipeline("image-text-to-text", model="MiniMaxAI/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("MiniMaxAI/MiniMax-M3", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("MiniMaxAI/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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MiniMaxAI/MiniMax-M3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MiniMaxAI/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": "MiniMaxAI/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/MiniMaxAI/MiniMax-M3

SGLang

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

请问A100可以部署吗?计算卡必须是 Hopper架构吗?

#15

by hxxxxxx13 - opened 2 days ago

Discussion

hxxxxxx13

2 days ago

请问A100或者4090可以部署吗? 量化版本也可以
我看vllm官方要求计算卡是** compute capability >= 9.0 (Hopper)**，但是A100 是 NVIDIA A100-SXM4-80GB, 8.0，之前用A100部署deepseek4-flsh就出现报错

VLLM要求

Prerequisites
● OS: Linux
● Python: 3.10 - 3.13
● NVIDIA: compute capability >= 9.0 (Hopper) recommended; 8x H200 / H20 for a tight single-node BF16 fit, or multi-node TP for long-context headroom
● AMD: MI350X/MI355X (gfx950), MI300X/MI325X (gfx942), ROCm 7.2+. BF16 needs TP=8; the MXFP8 variant runs from TP=4.
● --block-size 128 is mandatory on every platform (MSA sparse/index cache).

EclipseMist

2 days ago

请问A100或者4090可以部署吗? 量化版本也可以
我看vllm官方要求计算卡是** compute capability >= 9.0 (Hopper)**，但是A100 是 NVIDIA A100-SXM4-80GB, 8.0，之前用A100部署deepseek4-flsh就出现报错

VLLM要求
Prerequisites
● OS: Linux
● Python: 3.10 - 3.13
● NVIDIA: compute capability >= 9.0 (Hopper) recommended; 8x H200 / H20 for a tight single-node BF16 fit, or multi-node TP for long-context headroom
● AMD: MI350X/MI355X (gfx950), MI300X/MI325X (gfx942), ROCm 7.2+. BF16 needs TP=8; the MXFP8 variant runs from TP=4.
● --block-size 128 is mandatory on every platform (MSA sparse/index cache).

既然您使用的是基於 Ampere 架構的顯示卡，不妨試試支援 Minimax M3 的 vLLM 分支。

hxxxxxx13

1 day ago

请问A100或者4090可以部署吗? 量化版本也可以
我看vllm官方要求计算卡是** compute capability >= 9.0 (Hopper)**，但是A100 是 NVIDIA A100-SXM4-80GB, 8.0，之前用A100部署deepseek4-flsh就出现报错

VLLM要求
Prerequisites
● OS: Linux
● Python: 3.10 - 3.13
● NVIDIA: compute capability >= 9.0 (Hopper) recommended; 8x H200 / H20 for a tight single-node BF16 fit, or multi-node TP for long-context headroom
● AMD: MI350X/MI355X (gfx950), MI300X/MI325X (gfx942), ROCm 7.2+. BF16 needs TP=8; the MXFP8 variant runs from TP=4.
● --block-size 128 is mandatory on every platform (MSA sparse/index cache).
既然您使用的是基於 Ampere 架構的顯示卡，不妨試試支援 Minimax M3 的 vLLM 分支。

我没能找到适用SM80的VLLM分支

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