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
```

jiaxin commited on Oct 26, 2025

Commit

698aec0

1 Parent(s): 7496e98

update README

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -108,7 +108,7 @@ These comprehensive evaluations test real-world end-to-end coding and agentic to
 >- Terminal-Bench: All scores are evaluated with the official claude-code from the original [Terminal-Bench](https://www.tbench.ai/) repository(commit `94bf692`), averaged over 8 runs to report the mean pass rate.
 >- ArtifactsBench: All Scores are computed by averaging three runs with the official implementation of [ArtifactsBench](https://github.com/Tencent-Hunyuan/ArtifactsBenchmark), using the stable Gemini-2.5-Pro as the judge model.
 >- BrowseComp & BrowseComp-zh & GAIA (text only) & xbench-DeepSearch: All scores reported use the same agent framework as [WebExplorer](https://arxiv.org/pdf/2509.06501) (Liu et al. 2025), with minor tools description adjustment. We use the 103-sample text-only GAIA validation subset following [WebExplorer](https://arxiv.org/pdf/2509.06501) (Liu et al. 2025).
->- HLE (w/ tools): All reported scores are obtained using search tools and a Python tool. The search tools employ the same agent framework as [WebExplorer](https://arxiv.org/pdf/2509.06501) (Liu et al. 2025), and the Python tool runs in a Jupyter environment.
 >- τ²-Bench: All scores reported use "extended thinking with tool use", and employ GPT-4.1 as the user simulator.
 >- FinSearchComp-global: Official results are reported for GPT-5-Thinking, Gemini 2.5 Pro, and Kimi-K2. Other models are evaluated using the open-source [FinSearchComp](https://arxiv.org/pdf/2509.13160) (Hu et al. 2025) framework using both  search and Python tools, launched simultaneously for consistency.
 >- AgentCompany: All scores reported use OpenHands 0.42 agent framework.

 >- Terminal-Bench: All scores are evaluated with the official claude-code from the original [Terminal-Bench](https://www.tbench.ai/) repository(commit `94bf692`), averaged over 8 runs to report the mean pass rate.
 >- ArtifactsBench: All Scores are computed by averaging three runs with the official implementation of [ArtifactsBench](https://github.com/Tencent-Hunyuan/ArtifactsBenchmark), using the stable Gemini-2.5-Pro as the judge model.
 >- BrowseComp & BrowseComp-zh & GAIA (text only) & xbench-DeepSearch: All scores reported use the same agent framework as [WebExplorer](https://arxiv.org/pdf/2509.06501) (Liu et al. 2025), with minor tools description adjustment. We use the 103-sample text-only GAIA validation subset following [WebExplorer](https://arxiv.org/pdf/2509.06501) (Liu et al. 2025).
+>- HLE (w/ tools): All reported scores are obtained using search tools and a Python tool. The search tools employ the same agent framework as [WebExplorer](https://arxiv.org/pdf/2509.06501) (Liu et al. 2025), and the Python tool runs in a Jupyter environment. We use the text-only HLE subset.
 >- τ²-Bench: All scores reported use "extended thinking with tool use", and employ GPT-4.1 as the user simulator.
 >- FinSearchComp-global: Official results are reported for GPT-5-Thinking, Gemini 2.5 Pro, and Kimi-K2. Other models are evaluated using the open-source [FinSearchComp](https://arxiv.org/pdf/2509.13160) (Hu et al. 2025) framework using both  search and Python tools, launched simultaneously for consistency.
 >- AgentCompany: All scores reported use OpenHands 0.42 agent framework.