Instructions to use dlothian/MiniMax-M2.7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dlothian/MiniMax-M2.7 with Transformers:

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

pipe = pipeline("text-generation", model="dlothian/MiniMax-M2.7", 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("dlothian/MiniMax-M2.7", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("dlothian/MiniMax-M2.7", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use dlothian/MiniMax-M2.7 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dlothian/MiniMax-M2.7"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dlothian/MiniMax-M2.7",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dlothian/MiniMax-M2.7

SGLang

How to use dlothian/MiniMax-M2.7 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 "dlothian/MiniMax-M2.7" \
    --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": "dlothian/MiniMax-M2.7",
		"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 "dlothian/MiniMax-M2.7" \
        --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": "dlothian/MiniMax-M2.7",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dlothian/MiniMax-M2.7 with Docker Model Runner:
```
docker model run hf.co/dlothian/MiniMax-M2.7
```

MiniMax-M2.7 / docs /transformers_deploy_guide_cn.md

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MiniMax M2.7 模型 Transformers 部署指南

英文版 | 中文版

本文档适用模型

本文档适用以下模型，只需在部署时修改模型名称即可。

以下以 MiniMax-M2.7 为例说明部署流程。

环境要求

OS：Linux
Python：3.9 - 3.12
Transformers: 4.57.1
GPU：
- compute capability 7.0 or higher
- 显存需求：权重需要 220 GB

使用 Python 部署

建议使用虚拟环境（如 venv、conda、uv）以避免依赖冲突。

建议在全新的 Python 环境中安装 Transformers:

uv pip install transformers==4.57.1 torch accelerate --torch-backend=auto

运行如下 Python 命令运行模型，Transformers 会自动从 Huggingface 下载并缓存 MiniMax-M2.7 模型。

from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import torch

MODEL_PATH = "MiniMaxAI/MiniMax-M2.7"

model = AutoModelForCausalLM.from_pretrained(
    MODEL_PATH,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)

messages = [
    {"role": "user", "content": [{"type": "text", "text": "What is your favourite condiment?"}]},
    {"role": "assistant", "content": [{"type": "text", "text": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}]},
    {"role": "user", "content": [{"type": "text", "text": "Do you have mayonnaise recipes?"}]}
]

model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to("cuda")

generated_ids = model.generate(model_inputs, max_new_tokens=100, generation_config=model.generation_config)

response = tokenizer.batch_decode(generated_ids)[0]

print(response)

常见问题

Huggingface 网络问题

如果遇到网络问题，可以设置代理后再进行拉取。

export HF_ENDPOINT=https://hf-mirror.com

MiniMax-M2 model is not currently supported

请确认开启 trust_remote_code=True。

获取支持

如果在部署 MiniMax 模型过程中遇到任何问题：

通过邮箱 model@minimax.io 等官方渠道联系我们的技术支持团队
在我们的 GitHub 仓库提交 Issue
通过我们的官方企业微信交流群反馈

我们会持续优化模型的部署体验，欢迎反馈！