Latest ContentInference Cookbook Model Library

Phi 4 Mini Reasoning

Phi-4-mini-reasoning is a lightweight dense transformer model optimized for multi-step mathematical and logical reasoning with agentic tool-calling capabilities in constrained environments. It features 3.8B parameters with 32 layers, 3,072 hidden size, and 24 attention heads using grouped-query attention with 8 key-value heads. The model supports a 128K token context window with long RoPE scaling and a sliding window of 262K tokens, along with a 200K vocabulary. Trained on ~150B tokens, it delivers efficient, low-latency structured problem solving.

Type	Dense LLM
Capabilities	Text Generation, Instruction Following, Reasoning, Mathematical Reasoning+5 more
Group Release Date	December 11, 2024
Links	Paper\|Blog\|HF Model Card
License	MIT

Inference Instructions

Deploy and run this model on NVIDIA B200 GPUs using the command below. Copy the command to get started with inference.

CONSOLE

docker run --gpus all 
 --shm-size 128g 
 -p 8000:8000 
 -v ~/.cache/huggingface:/root/.cache/huggingface 
 -e HF_TOKEN='YOUR_HF_TOKEN' 
 --ipc=host 
 lmsysorg/sglang:v0.5.9 
 python3 -m sglang.launch_server 
 --model-path microsoft/Phi-4-mini-reasoning 
 --host 0.0.0.0 
 --port 8000 
 --max-running-requests 1024 
 --max-prefill-tokens 65536 
 --tp 8 
 --enable-piecewise-cuda-graph 
 --mem-fraction-static 0.95 
 --trust-remote-code