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
```

Clarify supported context length in docs

#21

by rogeryoungh - opened Jan 28

base: refs/heads/main

←

from: refs/pr/21

Discussion Files changed

+16

-8

Files changed (4) hide show

docs/sglang_deploy_guide.md +4 -2
docs/sglang_deploy_guide_cn.md +4 -2
docs/vllm_deploy_guide.md +4 -2
docs/vllm_deploy_guide_cn.md +4 -2

docs/sglang_deploy_guide.md CHANGED Viewed

@@ -27,9 +27,11 @@ The deployment process is illustrated below using MiniMax-M2.1 as an example.
 The following are recommended configurations; actual requirements should be adjusted based on your use case:
-- 4x 96GB GPUs: Supported context length of up to 400K tokens.
-- 8x 144GB GPUs: Supported context length of up to 3M tokens.
 ## Deployment with Python

 The following are recommended configurations; actual requirements should be adjusted based on your use case:
+- **96G x4** GPU: Supports a total KV Cache capacity of 400K tokens.
+- **144G x8** GPU: Supports a total KV Cache capacity of up to 3M tokens.
+> **Note**: The values above represent the total aggregate hardware KV Cache capacity. The maximum context length per individual sequence remains **196K** tokens.
 ## Deployment with Python

docs/sglang_deploy_guide_cn.md CHANGED Viewed

@@ -27,9 +27,11 @@
 以下为推荐配置，实际需求请根据业务场景调整：
-- 96G x4 GPU：支持 40 万 token 的总上下文。
-- 144G x8 GPU：支持长达 300 万 token 的总上下文。
 ## 使用 Python 部署

 以下为推荐配置，实际需求请根据业务场景调整：
+- **96G x4 GPU**：总 KV Cache 容量支持 40 万 token。
+- **144G x8 GPU**：总 KV Cache 容量支持高达 300 万 token。
+> **注**：以上数值为硬件支持的最大并发缓存总量，模型单序列（Single Sequence）长度上限仍为 196k。
 ## 使用 Python 部署

docs/vllm_deploy_guide.md CHANGED Viewed

@@ -26,9 +26,11 @@ The deployment process is illustrated below using MiniMax-M2.1 as an example.
 The following are recommended configurations; actual requirements should be adjusted based on your use case:
-- 4x 96GB GPUs: Supported context length of up to 400K tokens.
-- 8x 144GB GPUs: Supported context length of up to 3M tokens.
 ## Deployment with Python

 The following are recommended configurations; actual requirements should be adjusted based on your use case:
+- **96G x4** GPU: Supports a total KV Cache capacity of 400K tokens.
+- **144G x8** GPU: Supports a total KV Cache capacity of up to 3M tokens.
+> **Note**: The values above represent the total aggregate hardware KV Cache capacity. The maximum context length per individual sequence remains **196K** tokens.
 ## Deployment with Python

docs/vllm_deploy_guide_cn.md CHANGED Viewed

@@ -26,9 +26,11 @@
 以下为推荐配置，实际需求请根据业务场景调整：
-- 96G x4 GPU：支持 40 万 token 的总上下文。
-- 144G x8 GPU：支持长达 300 万 token 的总上下文。
 ## 使用 Python 部署

 以下为推荐配置，实际需求请根据业务场景调整：
+- **96G x4 GPU**：总 KV Cache 容量支持 40 万 token。
+- **144G x8 GPU**：总 KV Cache 容量支持高达 300 万 token。
+> **注**：以上数值为硬件支持的最大并发缓存总量，模型单序列（Single Sequence）长度上限仍为 196k。
 ## 使用 Python 部署