MiniMax M2 Model Transformers Deployment Guide
English Version | Chinese Version
Applicable Models
This document applies to the following models. You only need to change the model name during deployment.
The deployment process is illustrated below using MiniMax-M2 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:
uv pip install transformers torch accelerate --torch-backend=auto
Run the following Python script to run the model. Transformers will automatically download and cache the MiniMax-M2 model from Hugging Face.
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import torch
MODEL_PATH = "MiniMaxAI/MiniMax-M2"
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=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.
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
Submit an issue on our GitHub repository
We continuously optimize the deployment experience for our models. Feedback is welcome!