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)

# pip install -U transformers accelerate
# 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, device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

Add IFBench evaluation result

#63

by SaylorTwift HF Staff - opened about 7 hours ago

base: refs/heads/main

←

from: refs/pr/63

Discussion Files changed

-0

SaylorTwift

about 7 hours ago

Hugging Face CLI

Add IFBench evaluation result

Summary

This PR adds the IFBench score reported for MiniMaxAI/MiniMax-M2 to .eval_results/, following the Hugging Face Hub evaluation-results specification. IFBench (allenai/IFBench_test) is now an official Hub benchmark, with prompt-level loose accuracy as its headline metric.

Benchmarks Added

IFBench: 72

Source

Model card: https://huggingface.co/MiniMaxAI/MiniMax-M2 (row: "IFBench (Metric (AA))")

Files Added

.eval_results/ifbench.yaml

Verification

This score is self-reported in the source above; no verified token is provided because it was not run via HF Jobs with inspect-ai. The Hub task is prompt-level loose accuracy (0-100). If your number was computed with a different IFBench variant (strict, or instruction-level), let us know and we will fix or close this PR.

Add IFBench evaluation result4471e26e

SaylorTwift

about 7 hours ago

Hugging Face CLI

Closing this: the source doesn't say which IFBench variant this score is (the Hub task is prompt-level loose accuracy), so we can't map it reliably. If you can confirm it's prompt-level loose, we're happy to reopen.

SaylorTwift changed pull request status to closed about 7 hours ago

SaylorTwift

about 7 hours ago

Hugging Face CLI

Reopening: keeping this score as reported. If it isn't prompt-level loose accuracy, let us know and we'll update or close it.

SaylorTwift changed pull request status to open about 7 hours ago

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