base_model: microsoft/Phi-4-mini-reasoning
language:
- en
library_name: transformers
license: mit
license_link: >-
https://huggingface.co/microsoft/Phi-4-mini-instruct-reasoning/resolve/main/LICENSE
pipeline_tag: text-generation
tags:
- nlp
- math
- code
- llama-cpp
- gguf-my-repo
widget:
- messages:
- role: user
content: How to solve 3*x^2+4*x+5=1?
Triangle104/Phi-4-mini-reasoning-Q4_K_M-GGUF
This model was converted to GGUF format from microsoft/Phi-4-mini-reasoning using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Phi-4-mini-reasoning is a lightweight open model built upon synthetic data with a focus on high-quality, reasoning dense data further finetuned for more advanced math reasoning capabilities. The model belongs to the Phi-4 model family and supports 128K token context length.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo Triangle104/Phi-4-mini-reasoning-Q4_K_M-GGUF --hf-file phi-4-mini-reasoning-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Triangle104/Phi-4-mini-reasoning-Q4_K_M-GGUF --hf-file phi-4-mini-reasoning-q4_k_m.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo Triangle104/Phi-4-mini-reasoning-Q4_K_M-GGUF --hf-file phi-4-mini-reasoning-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Triangle104/Phi-4-mini-reasoning-Q4_K_M-GGUF --hf-file phi-4-mini-reasoning-q4_k_m.gguf -c 2048