Instructions to use MiniMaxAI/MiniMax-M1-40k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MiniMaxAI/MiniMax-M1-40k with Transformers:

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

pipe = pipeline("text-generation", model="MiniMaxAI/MiniMax-M1-40k", 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-M1-40k", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MiniMaxAI/MiniMax-M1-40k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MiniMaxAI/MiniMax-M1-40k"
# 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-M1-40k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MiniMaxAI/MiniMax-M1-40k

SGLang

How to use MiniMaxAI/MiniMax-M1-40k 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-M1-40k" \
    --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-M1-40k",
		"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-M1-40k" \
        --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-M1-40k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MiniMaxAI/MiniMax-M1-40k with Docker Model Runner:
```
docker model run hf.co/MiniMaxAI/MiniMax-M1-40k
```

QscQ commited on Jun 14, 2025

Commit

17002c5

verified ·

1 Parent(s): a7365d6

Update function_call_guide_cn.md

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@@ -100,7 +100,7 @@ text = tokenizer.apply_chat_template(
 在模型内部处理时，函数定义会被转换为特殊格式并拼接到输入文本中：
 ```
-]~!b[]~b]system ai_setting=海螺AI
 MiniMax AI是由上海稀宇科技有限公司（MiniMax）自主研发的AI助理。[e~[
 ]~b]system tool_setting=tools
 You are provided with these tools:
@@ -115,7 +115,7 @@ If you need to call tools, please respond with <tool_calls></tool_calls> XML tag
 </tool_calls>[e~[
 ]~b]user name=用户
 OpenAI 和 Gemini 的最近一次发布会都是什么时候?[e~[
-]~b]ai name=海螺AI
 ```
 ### 模型输出格式

 在模型内部处理时，函数定义会被转换为特殊格式并拼接到输入文本中：
 ```
+]~!b[]~b]system ai_setting=MiniMax AI
 MiniMax AI是由上海稀宇科技有限公司（MiniMax）自主研发的AI助理。[e~[
 ]~b]system tool_setting=tools
 You are provided with these tools:
 </tool_calls>[e~[
 ]~b]user name=用户
 OpenAI 和 Gemini 的最近一次发布会都是什么时候?[e~[
+]~b]ai name=MiniMax AI
 ```
 ### 模型输出格式