Instructions to use mackkkkkilllll/Phi-4-mini-reasoning-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use mackkkkkilllll/Phi-4-mini-reasoning-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mackkkkkilllll/Phi-4-mini-reasoning-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf mackkkkkilllll/Phi-4-mini-reasoning-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mackkkkkilllll/Phi-4-mini-reasoning-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf mackkkkkilllll/Phi-4-mini-reasoning-GGUF:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf mackkkkkilllll/Phi-4-mini-reasoning-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf mackkkkkilllll/Phi-4-mini-reasoning-GGUF:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf mackkkkkilllll/Phi-4-mini-reasoning-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mackkkkkilllll/Phi-4-mini-reasoning-GGUF:F16

Use Docker

docker model run hf.co/mackkkkkilllll/Phi-4-mini-reasoning-GGUF:F16

LM Studio
Jan

vLLM

How to use mackkkkkilllll/Phi-4-mini-reasoning-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mackkkkkilllll/Phi-4-mini-reasoning-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mackkkkkilllll/Phi-4-mini-reasoning-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mackkkkkilllll/Phi-4-mini-reasoning-GGUF:F16

Ollama
How to use mackkkkkilllll/Phi-4-mini-reasoning-GGUF with Ollama:
```
ollama run hf.co/mackkkkkilllll/Phi-4-mini-reasoning-GGUF:F16
```
Unsloth Desktop

How to use mackkkkkilllll/Phi-4-mini-reasoning-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mackkkkkilllll/Phi-4-mini-reasoning-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mackkkkkilllll/Phi-4-mini-reasoning-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mackkkkkilllll/Phi-4-mini-reasoning-GGUF with Docker Model Runner:
```
docker model run hf.co/mackkkkkilllll/Phi-4-mini-reasoning-GGUF:F16
```

Lemonade

How to use mackkkkkilllll/Phi-4-mini-reasoning-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mackkkkkilllll/Phi-4-mini-reasoning-GGUF:F16

Run and chat with the model

lemonade run user.Phi-4-mini-reasoning-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use mackkkkkilllll/Phi-4-mini-reasoning-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mackkkkkilllll/Phi-4-mini-reasoning-GGUF:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default mackkkkkilllll/Phi-4-mini-reasoning-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mackkkkkilllll/Phi-4-mini-reasoning-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mackkkkkilllll/Phi-4-mini-reasoning-GGUF:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "mackkkkkilllll/Phi-4-mini-reasoning-GGUF:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Phi-4-mini-reasoning GGUF

GGUF conversion and Q4_K_M quantization of Microsoft Phi-4-mini-reasoning for efficient local inference.

Original Model

Official model:

https://huggingface.co/microsoft/Phi-4-mini-reasoning

This repository contains community-created GGUF conversions of the original model.

Please refer to the official model card for complete information about the model, training, intended use, limitations, and license.

Files

File	Format	Approx. Size	Use
`Phi-4-mini-reasoning-F16.gguf`	F16	~7.2 GB	Higher precision
`Phi-4-mini-reasoning-Q4_K_M.gguf`	Q4_K_M	~2.4 GB	Recommended

Recommended Version

Q4_K_M

Phi-4-mini-reasoning-Q4_K_M.gguf is recommended for most local inference setups.

It provides a much smaller memory footprint than the F16 version while maintaining a good balance between quality and efficiency.

F16

The F16 version is provided for users who have sufficient memory and want to use a higher-precision GGUF representation.

Quantization

Conversion pipeline:

Microsoft Phi-4-mini-reasoning → F16 GGUF → Q4_K_M

The Q4_K_M file was generated using the quantization tools from llama.cpp.

Quantization:

Q4_K_M

No importance matrix was used.

llama.cpp

Basic inference:

llama-cli -m Phi-4-mini-reasoning-Q4_K_M.gguf

GPU offloading:

llama-cli -m Phi-4-mini-reasoning-Q4_K_M.gguf -ngl 99

Run a local server:

llama-server -m Phi-4-mini-reasoning-Q4_K_M.gguf -ngl 99

Compatible Software

The GGUF files can be used with software supporting the GGUF format, including:

llama.cpp
llama-cpp-python
LM Studio
Jan
Other GGUF-compatible inference engines

Context

Phi-4-mini-reasoning is designed for reasoning-focused language generation.

For model-specific capabilities and limitations, refer to the official Microsoft model card.

License

The original model is provided under its respective Microsoft license.

Please review the official model repository before using or redistributing the model:

https://huggingface.co/microsoft/Phi-4-mini-reasoning

Disclaimer

This is a community GGUF conversion and quantization.

This repository is not an official Microsoft repository.

Conversion Environment

Conversion and quantization were performed using llama.cpp.

Conversion:

convert_hf_to_gguf.py

Quantization:

llama-quantize

Quantization type:

Q4_K_M

Downloads last month: 139

GGUF

Model size

4B params

Architecture

phi3

Hardware compatibility

4-bit

16-bit