Instructions to use MiniMaxAI/MiniMax-M1-80k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MiniMaxAI/MiniMax-M1-80k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MiniMaxAI/MiniMax-M1-80k", 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-M1-80k", trust_remote_code=True, device_map="auto") - Inference
- HuggingChat
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MiniMaxAI/MiniMax-M1-80k with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MiniMaxAI/MiniMax-M1-80k" # 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-M1-80k", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/MiniMaxAI/MiniMax-M1-80k
- SGLang
How to use MiniMaxAI/MiniMax-M1-80k 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-M1-80k" \ --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-M1-80k", "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-M1-80k" \ --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-M1-80k", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use MiniMaxAI/MiniMax-M1-80k with Docker Model Runner:
docker model run hf.co/MiniMaxAI/MiniMax-M1-80k
Download MiniMax_M1_tech_report.pdf from MiniMaxAI/MiniMax-M1-80k: direct link, hf CLI and curl.
- Browser
- Download file 1.48 MB
-
https://huggingface.co/MiniMaxAI/MiniMax-M1-80k/resolve/main/MiniMax_M1_tech_report.pdf
- Command line
-
hf download hf://MiniMaxAI/MiniMax-M1-80k/MiniMax_M1_tech_report.pdf
-
curl -L -o MiniMax_M1_tech_report.pdf https://huggingface.co/MiniMaxAI/MiniMax-M1-80k/resolve/main/MiniMax_M1_tech_report.pdf
1.48 MB
- Xet hash:
- 27dece9c26488cca62cfd6d04c533f8a733bca44c2d6521e5fda659541203303
- Size of remote file:
- 1.48 MB
- SHA256:
- a159321f78136a4d411a6eb121fba6dda790f2810e62445f48c15fc245e11df0
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