Text Generation
Transformers
Safetensors
English
minimax_m2
Mixture of Experts
mixture-of-experts
quantization
nvfp4
fp4
fp8
reap
pruned
minimax
minimax-m2
blackwell
dgx-spark
vllm
conversational
custom_code
8-bit precision
modelopt
Instructions to use catplusplus/MiniMax-M2.7-REAP-172B-A10B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use catplusplus/MiniMax-M2.7-REAP-172B-A10B-NVFP4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="catplusplus/MiniMax-M2.7-REAP-172B-A10B-NVFP4", 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("catplusplus/MiniMax-M2.7-REAP-172B-A10B-NVFP4", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("catplusplus/MiniMax-M2.7-REAP-172B-A10B-NVFP4", trust_remote_code=True, device_map="auto") 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 Settings
- vLLM
How to use catplusplus/MiniMax-M2.7-REAP-172B-A10B-NVFP4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "catplusplus/MiniMax-M2.7-REAP-172B-A10B-NVFP4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "catplusplus/MiniMax-M2.7-REAP-172B-A10B-NVFP4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/catplusplus/MiniMax-M2.7-REAP-172B-A10B-NVFP4
- SGLang
How to use catplusplus/MiniMax-M2.7-REAP-172B-A10B-NVFP4 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 "catplusplus/MiniMax-M2.7-REAP-172B-A10B-NVFP4" \ --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": "catplusplus/MiniMax-M2.7-REAP-172B-A10B-NVFP4", "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 "catplusplus/MiniMax-M2.7-REAP-172B-A10B-NVFP4" \ --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": "catplusplus/MiniMax-M2.7-REAP-172B-A10B-NVFP4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use catplusplus/MiniMax-M2.7-REAP-172B-A10B-NVFP4 with Docker Model Runner:
docker model run hf.co/catplusplus/MiniMax-M2.7-REAP-172B-A10B-NVFP4
| SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)" | |
| MODEL_DIR="$(dirname "$SCRIPT_DIR")" | |
| . ~/venv/vllm/bin/activate | |
| dockless destroy seeker-inference | |
| sleep 5 | |
| dockless run -d --name seeker-inference \ | |
| -e CUDA_HOME=/usr/local/cuda-13.0 \ | |
| -e C_INCLUDE_PATH=/usr/local/cuda-13.0/include \ | |
| -e LIBRARY_PATH=/usr/lib/aarch64-linux-gnu/nvidia \ | |
| -e FLASHINFER_NVCC=/usr/local/cuda-13.0/bin/nvcc \ | |
| -e VLLM_USE_FLASHINFER_MOE_FP4=0 "$SCRIPT_DIR/unglitched_vllm" \ | |
| --served-model-name Nikola \ | |
| --port 9000 \ | |
| --enable-auto-tool-choice \ | |
| --tool-call-parser minimax_m2 \ | |
| --reasoning-parser minimax_m2_optthink \ | |
| --reasoning-parser-plugin "$SCRIPT_DIR/minimax_m2_optthink_reasoning_parser.py" \ | |
| --enable-prefix-caching \ | |
| --max-num-seqs 4 --cudagraph-capture-sizes 1 2 4 --max-model-len auto \ | |
| --max_num_batched_tokens 8192 \ | |
| --gpu-memory-utilization 0.95 \ | |
| --attention-backend FLASHINFER \ | |
| --async-scheduling \ | |
| --enable-chunked-prefill \ | |
| --chat-template "$SCRIPT_DIR/chat_template.jinja" \ | |
| --model "${@:-$MODEL_DIR}" | |