Instructions to use MiniMaxAI/MiniMax-M2.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MiniMaxAI/MiniMax-M2.1 with Transformers:

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

pipe = pipeline("text-generation", model="MiniMaxAI/MiniMax-M2.1", 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-M2.1", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("MiniMaxAI/MiniMax-M2.1", 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-M2.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MiniMaxAI/MiniMax-M2.1"
# 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-M2.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MiniMaxAI/MiniMax-M2.1

SGLang

How to use MiniMaxAI/MiniMax-M2.1 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-M2.1" \
    --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-M2.1",
		"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-M2.1" \
        --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-M2.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MiniMaxAI/MiniMax-M2.1 with Docker Model Runner:
```
docker model run hf.co/MiniMaxAI/MiniMax-M2.1
```

Loading weigts error when running MiniMax-2.1 with sglang using pipeline parallelism

#18

by tuo02 - opened Jan 13

Discussion

tuo02

Jan 13

When I`m running MiniMax-2.1 on H200 with sglang command:
python3 -m sglang.launch_server --model /volume/models/MiniMaxAI/MiniMax-M2.1/snapshots/17f852dfda7d63c61b1107d47552bb30488ffbee --trust-remote-code --tp-size 2 --pp-size 2 --mem-fraction-static 0.85 --chunked-prefill-size 20480 --page-size 64 --cuda-graph-max-bs 64 --enable-metrics

the error occurs:

[2026-01-12 02:39:23 PP3 TP1] Scheduler hit an exception: Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/sglang/srt/managers/scheduler.py", line 2653, in run_scheduler_process
    scheduler = Scheduler(
  File "/usr/local/lib/python3.10/dist-packages/sglang/srt/managers/scheduler.py", line 316, in __init__
    self.tp_worker = TpModelWorker(
  File "/usr/local/lib/python3.10/dist-packages/sglang/srt/managers/tp_worker.py", line 245, in __init__
    self._model_runner = ModelRunner(
  File "/usr/local/lib/python3.10/dist-packages/sglang/srt/model_executor/model_runner.py", line 361, in __init__
    self.initialize(min_per_gpu_memory)
  File "/usr/local/lib/python3.10/dist-packages/sglang/srt/model_executor/model_runner.py", line 436, in initialize
    self.load_model()
  File "/usr/local/lib/python3.10/dist-packages/sglang/srt/model_executor/model_runner.py", line 804, in load_model
    self.model = get_model(
  File "/usr/local/lib/python3.10/dist-packages/sglang/srt/model_loader/__init__.py", line 28, in get_model
    return loader.load_model(
  File "/usr/local/lib/python3.10/dist-packages/sglang/srt/model_loader/loader.py", line 600, in load_model
    self.load_weights_and_postprocess(
  File "/usr/local/lib/python3.10/dist-packages/sglang/srt/model_loader/loader.py", line 608, in load_weights_and_postprocess
    model.load_weights(weights)
  File "/usr/local/lib/python3.10/dist-packages/sglang/srt/models/minimax_m2.py", line 882, in load_weights
    param = params_dict[name]
KeyError: 'model.layers.37.block_sparse_moe.e_score_correction_bias'

[2026-01-12 02:39:23] Received sigquit from a child process. It usually means the child failed.

rogeryoungh

MiniMax org Jan 13

PP is not supported for M2 in vLLM and SGLang currently. We recommend using TP or EP instead, as they have been fully implemented and verified.

tuo02

Jan 22

PP is not supported for M2 in vLLM and SGLang currently. We recommend using TP or EP instead, as they have been fully implemented and verified.

Do you deploy M2 on RTX-5090 ? Do you have any recommended parameter configurations?

DarrenChen

Jan 26

PP is not supported for M2 in vLLM and SGLang currently. We recommend using TP or EP instead, as they have been fully implemented and verified.

Are there any version requirements for vllm and sglang? I hope there are more parameters to refer to.

rogeryoungh

MiniMax org Jan 28

Are there any version requirements for vllm and sglang? I hope there are more parameters to refer to.

@DarrenChen We recommend using the latest release of vLLM/SGLang, as our optimizations and verification for M2.1/M2 are primarily focused on the most recent official versions.

Do you deploy M2 on RTX-5090 ? Do you have any recommended parameter configurations?

@tuo02 For detailed deployment parameters and recommended configurations (e.g., 4-GPU or 8-GPU setups), please refer to our documentation here: vllm, sglang.

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