Instructions to use TitleOS/HomePhi4_4B_Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TitleOS/HomePhi4_4B_Q4_K_M-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TitleOS/HomePhi4_4B_Q4_K_M-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("TitleOS/HomePhi4_4B_Q4_K_M-GGUF", dtype="auto")

llama-cpp-python

How to use TitleOS/HomePhi4_4B_Q4_K_M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="TitleOS/HomePhi4_4B_Q4_K_M-GGUF",
	filename="homephi4_4b_merged-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use TitleOS/HomePhi4_4B_Q4_K_M-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf TitleOS/HomePhi4_4B_Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf TitleOS/HomePhi4_4B_Q4_K_M-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf TitleOS/HomePhi4_4B_Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf TitleOS/HomePhi4_4B_Q4_K_M-GGUF:Q4_K_M

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 TitleOS/HomePhi4_4B_Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf TitleOS/HomePhi4_4B_Q4_K_M-GGUF:Q4_K_M

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 TitleOS/HomePhi4_4B_Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TitleOS/HomePhi4_4B_Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/TitleOS/HomePhi4_4B_Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use TitleOS/HomePhi4_4B_Q4_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TitleOS/HomePhi4_4B_Q4_K_M-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": "TitleOS/HomePhi4_4B_Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TitleOS/HomePhi4_4B_Q4_K_M-GGUF:Q4_K_M

SGLang

How to use TitleOS/HomePhi4_4B_Q4_K_M-GGUF 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 "TitleOS/HomePhi4_4B_Q4_K_M-GGUF" \
    --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": "TitleOS/HomePhi4_4B_Q4_K_M-GGUF",
		"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 "TitleOS/HomePhi4_4B_Q4_K_M-GGUF" \
        --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": "TitleOS/HomePhi4_4B_Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use TitleOS/HomePhi4_4B_Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/TitleOS/HomePhi4_4B_Q4_K_M-GGUF:Q4_K_M
```

Unsloth Studio new

How to use TitleOS/HomePhi4_4B_Q4_K_M-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for TitleOS/HomePhi4_4B_Q4_K_M-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for TitleOS/HomePhi4_4B_Q4_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for TitleOS/HomePhi4_4B_Q4_K_M-GGUF to start chatting

Pi new

How to use TitleOS/HomePhi4_4B_Q4_K_M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf TitleOS/HomePhi4_4B_Q4_K_M-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "TitleOS/HomePhi4_4B_Q4_K_M-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use TitleOS/HomePhi4_4B_Q4_K_M-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf TitleOS/HomePhi4_4B_Q4_K_M-GGUF:Q4_K_M

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 TitleOS/HomePhi4_4B_Q4_K_M-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use TitleOS/HomePhi4_4B_Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/TitleOS/HomePhi4_4B_Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use TitleOS/HomePhi4_4B_Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TitleOS/HomePhi4_4B_Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.HomePhi4_4B_Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

TitleOS/HomePhi4_4B

HomePhi4_4B-Q4_K_M is a fine-tuned version of Microsoft's Phi-4 Mini Reasoning (3.8B parameters), specifically optimized for controlling Home Assistant instances via natural language, which was then merged and quantized to Q4.

It has been fine-tuned on the acon96/Home-Assistant-Requests dataset to excel at interpreting user intent and generating accurate JSON function calls to control smart home devices (lights, fans, switches, etc.). This model is designed to be small enough to run locally on edge hardware (like an N100 or Raspberry Pi 5 with 8GB RAM) while maintaining high reasoning capabilities.

Model Details

Model Architecture: Phi-4 Mini (Dense Decoder-only Transformer)
Parameters: ~3.8 Billion
Context Length: 128k tokens (Effective fine-tuning context: ~4096)
Base Model: microsoft/Phi-4-mini-instruct
Fine-tuning Dataset: acon96/Home-Assistant-Requests
License: MPL-2.0

Intended Use

This model is strictly intended for local smart home control. It acts as a natural language interface for Home Assistant, translating requests like "Turn off the kitchen lights" into structured JSON commands that Home Assistant integrations (like llama_conversation or Extended OpenAI Conversation) can execute.

It is not a general-purpose assistant and may hallucinate if asked about history, math, or coding outside the scope of home automation.

Prompt Format

To get the best performance, you must adhere to the prompt format used during training. The model expects a system prompt that defines its persona and lists the available devices and tools.

System Prompt Template:

I want you to act as smart home expert manager of Home Assistant.

Current Time: {{now()}}

Available Devices:
```csv
entity_id,name,state,aliases
{% for entity in exposed_entities -%}
{{ entity.entity_id }},{{ entity.name }},{{ entity.state }},{{entity.aliases | join('/')}}
{% endfor -%}

You have access to the internet and other tools. The current state of devices is provided in available devices. Use execute_services function only for requested action, not for current states. Do not execute service without user's confirmation. Do not restate or appreciate what user says, rather make a quick inquiry. In addition, answer questions about the world, news, and general knowledge when requested. If requested by the user your name is AI. If a request appears to be an accident or otherwise doesn't make sense, reply with "Canceled".