Instructions to use MiniMaxAI/MiniMax-M2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MiniMaxAI/MiniMax-M2 with Transformers:

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

pipe = pipeline("text-generation", model="MiniMaxAI/MiniMax-M2", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MiniMaxAI/MiniMax-M2", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("MiniMaxAI/MiniMax-M2", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MiniMaxAI/MiniMax-M2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MiniMaxAI/MiniMax-M2"
# 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-M2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MiniMaxAI/MiniMax-M2

SGLang

How to use MiniMaxAI/MiniMax-M2 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-M2" \
    --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-M2",
		"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 "MiniMaxAI/MiniMax-M2" \
        --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-M2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MiniMaxAI/MiniMax-M2 with Docker Model Runner:
```
docker model run hf.co/MiniMaxAI/MiniMax-M2
```

zhaochenyang20 commited on Oct 26, 2025

Commit

17642e0

verified ·

1 Parent(s): c4075e3

Update docs/sglang_deploy_guide.md

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Files changed (1) hide show

docs/sglang_deploy_guide.md +6 -8

docs/sglang_deploy_guide.md CHANGED Viewed

@@ -32,7 +32,7 @@ The following are recommended configurations; actual requirements should be adju
 It is recommended to use a virtual environment (such as **venv**, **conda**, or **uv**) to avoid dependency conflicts.
-We recommend installing SGLang in a fresh Python environment. Since it has not been released yet, you need to manually build it from the source code:
 ```bash
 git clone -b v0.5.4.post3 https://github.com/sgl-project/sglang.git
@@ -56,7 +56,7 @@ python -m sglang.launch_server \
     --host 0.0.0.0 \
     --trust-remote-code \
     --port 8000 \
-    --mem-fraction-static 0.7
 ```
 8-GPU deployment command:
@@ -67,15 +67,13 @@ python -m sglang.launch_server \
     --tp-size 8 \
     --ep-size 8 \
     --tool-call-parser minimax-m2 \
-    --reasoning-parser minimax-append-think \
-    --host 0.0.0.0 \
     --trust-remote-code \
     --port 8000 \
-    --mem-fraction-static 0.7
 ```
 ## Testing Deployment
 After startup, you can test the SGLang OpenAI-compatible API with the following command:
@@ -104,7 +102,7 @@ export HF_ENDPOINT=https://hf-mirror.com
 ### MiniMax-M2 model is not currently supported
-This SGLang version is outdated. Please upgrade to the latest version.
 ## Getting Support

 It is recommended to use a virtual environment (such as **venv**, **conda**, or **uv**) to avoid dependency conflicts.
+We recommend installing SGLang in a fresh Python environment:
 ```bash
 git clone -b v0.5.4.post3 https://github.com/sgl-project/sglang.git
     --host 0.0.0.0 \
     --trust-remote-code \
     --port 8000 \
+    --mem-fraction-static 0.85
 ```
 8-GPU deployment command:
     --tp-size 8 \
     --ep-size 8 \
     --tool-call-parser minimax-m2 \
     --trust-remote-code \
+    --host 0.0.0.0 \
+    --reasoning-parser minimax-append-think \
     --port 8000 \
+--mem-fraction-static 0.85
 ```
 ## Testing Deployment
 After startup, you can test the SGLang OpenAI-compatible API with the following command:
 ### MiniMax-M2 model is not currently supported
+Please upgrade to the latest stable version, >= v0.5.4.post3.
 ## Getting Support