Instructions to use MiniMaxAI/MiniMax-M2.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MiniMaxAI/MiniMax-M2.5 with Transformers:

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

pipe = pipeline("text-generation", model="MiniMaxAI/MiniMax-M2.5", 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-M2.5", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("MiniMaxAI/MiniMax-M2.5", 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-M2.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MiniMaxAI/MiniMax-M2.5"
# 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-M2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MiniMaxAI/MiniMax-M2.5

SGLang

How to use MiniMaxAI/MiniMax-M2.5 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-M2.5" \
    --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-M2.5",
		"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-M2.5" \
        --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-M2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MiniMaxAI/MiniMax-M2.5 with Docker Model Runner:
```
docker model run hf.co/MiniMaxAI/MiniMax-M2.5
```

MiniMax-M2.5 / docs /transformers_deploy_guide.md

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	# MiniMax M2.5 Model Transformers Deployment Guide

	[English Version](./transformers_deploy_guide.md) \| [Chinese Version](./transformers_deploy_guide_cn.md)

	## Applicable Models

	This document applies to the following models. You only need to change the model name during deployment.

	- [MiniMaxAI/MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5)
	- [MiniMaxAI/MiniMax-M2.1](https://huggingface.co/MiniMaxAI/MiniMax-M2.1)
	- [MiniMaxAI/MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2)

	The deployment process is illustrated below using MiniMax-M2.5 as an example.

	## System Requirements

	- OS: Linux

	- Python: 3.9 - 3.12

	- Transformers: 4.57.1

	- GPU:

	- compute capability 7.0 or higher

	- Memory requirements: 220 GB for weights.

	## Deployment with Python

	It is recommended to use a virtual environment (such as venv, conda, or uv) to avoid dependency conflicts.

	We recommend installing Transformers in a fresh Python environment:

	```bash
	uv pip install transformers==4.57.1 torch accelerate --torch-backend=auto
	```

	Run the following Python script to run the model. Transformers will automatically download and cache the MiniMax-M2.5 model from Hugging Face.

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
	import torch

	MODEL_PATH = "MiniMaxAI/MiniMax-M2.5"

	model = AutoModelForCausalLM.from_pretrained(
	MODEL_PATH,
	device_map="auto",
	trust_remote_code=True,
	)
	tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)

	messages = [
	{"role": "user", "content": [{"type": "text", "text": "What is your favourite condiment?"}]},
	{"role": "assistant", "content": [{"type": "text", "text": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}]},
	{"role": "user", "content": [{"type": "text", "text": "Do you have mayonnaise recipes?"}]}
	]

	model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to("cuda")

	generated_ids = model.generate(model_inputs, max_new_tokens=100, generation_config=model.generation_config)

	response = tokenizer.batch_decode(generated_ids)[0]

	print(response)
	```

	## Common Issues

	### Hugging Face Network Issues

	If you encounter network issues, you can set up a proxy before pulling the model.

	```bash
	export HF_ENDPOINT=https://hf-mirror.com
	```

	### MiniMax-M2 model is not currently supported

	Please check that trust_remote_code=True.

	## Getting Support

	If you encounter any issues while deploying the MiniMax model:

	- Contact our technical support team through official channels such as email at [model@minimax.io](mailto:model@minimax.io)

	- Submit an issue on our [GitHub](https://github.com/MiniMax-AI) repository

	We continuously optimize the deployment experience for our models. Feedback is welcome!