Instructions to use MiniMaxAI/MiniMax-M2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
update guide
Browse files
docs/sglang_deploy_guide_cn.md
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- 通过邮箱 [model@minimax.io](mailto:model@minimax.io) 等官方渠道联系我们的技术支持团队
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- 在我们的 [GitHub](https://github.com/MiniMax-AI) 仓库提交 Issue
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我们会持续优化模型的部署体验,欢迎反馈!
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- 在我们的 [GitHub](https://github.com/MiniMax-AI) 仓库提交 Issue
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- 通过我们的 [M2企业微信群](https://huggingface.co/MiniMaxAI/MiniMax-M2/blob/main/figures/wechat.jpeg) 反馈
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docs/tool_calling_guide_cn.md
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- [MiniMax-M2 模型仓库](https://github.com/MiniMax-AI/MiniMax-M2)
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- [vLLM 项目主页](https://github.com/vllm-project/vllm)
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- [SGLang 项目主页](https://github.com/sgl-project/sglang)
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- [OpenAI Python SDK](https://github.com/openai/openai-python)
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- [MiniMax-M2 模型仓库](https://github.com/MiniMax-AI/MiniMax-M2)
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- [vLLM 项目主页](https://github.com/vllm-project/vllm)
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- [SGLang 项目主页](https://github.com/sgl-project/sglang)
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- [OpenAI Python SDK](https://github.com/openai/openai-python)
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## 获取支持
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如果遇到任何问题:
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- 通过邮箱 [model@minimax.io](mailto:model@minimax.io) 等官方渠道联系我们的技术支持团队
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- 在我们的仓库提交 Issue
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- 通过我们的[M2企业微信群](https://huggingface.co/MiniMaxAI/MiniMax-M2/blob/main/figures/wechat.jpeg) 反馈
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我们会持续优化模型的使用体验,欢迎反馈!
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docs/vllm_deploy_guide_cn.md
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我们会持续优化模型的部署体验,欢迎反馈!
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- 通过邮箱 [model@minimax.io](mailto:model@minimax.io) 等官方渠道联系我们的技术支持团队
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- 在我们的 [GitHub](https://github.com/MiniMax-AI) 仓库提交 Issue
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我们会持续优化模型的部署体验,欢迎反馈!
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