Instructions to use MiniMaxAI/MiniMax-M1-80k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MiniMaxAI/MiniMax-M1-80k with Transformers:

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

pipe = pipeline("text-generation", model="MiniMaxAI/MiniMax-M1-80k", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("MiniMaxAI/MiniMax-M1-80k", trust_remote_code=True, dtype="auto")

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MiniMaxAI/MiniMax-M1-80k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MiniMaxAI/MiniMax-M1-80k"
# 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-M1-80k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MiniMaxAI/MiniMax-M1-80k

SGLang

How to use MiniMaxAI/MiniMax-M1-80k 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-M1-80k" \
    --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-M1-80k",
		"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-M1-80k" \
        --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-M1-80k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MiniMaxAI/MiniMax-M1-80k with Docker Model Runner:
```
docker model run hf.co/MiniMaxAI/MiniMax-M1-80k
```

Updated library_name and tags

#11

by sergiopaniego HF Staff - opened Jun 24, 2025

base: refs/heads/main

←

from: refs/pr/11

Discussion Files changed

+223

-220

sergiopaniego

Jun 24, 2025

Hi! 👋

Sergio from HF here!

In this PR:

Added transformers to the library_name field to enable the "Use this model" button.
This provides users with a ready-to-use code snippet they can copy and paste directly — making adoption easier and improving model discoverability on the Hub.
Added vllm to the tags to highlight it as a supported deployment option and enhance visibility across relevant searches.

Updated library_name and tags47eb4540

sergiopaniego changed pull request title from Updated library_name and tags to Updated `library_name` and `tags` Jun 24, 2025

sergiopaniego changed pull request title from Updated `library_name` and `tags` to Updated library_name and tags Jun 24, 2025

sriting

MiniMax org Jun 24, 2025

@sergiopaniego Thanks, Sergio!
Really appreciate the PR!
Quick favor: could you make the same update for https://huggingface.co/MiniMaxAI/MiniMax-M1-40k? It’d keep both variants in sync and just as easy to adopt. Thanks again!

sriting changed pull request status to merged Jun 24, 2025

sergiopaniego

Jun 24, 2025

Sure! Created: https://huggingface.co/MiniMaxAI/MiniMax-M1-40k/edit/main/README.md

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