Libraries llama-cpp-python How to use KellanF89/phi4-cybersecurity-quantized with llama-cpp-python:
# !pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="KellanF89/phi4-cybersecurity-quantized",
filename="phi4-cybersecurity-gguf-unquantized.gguf",
)
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
) Notebooks Google Colab Kaggle Local Apps Settings llama.cpp How to use KellanF89/phi4-cybersecurity-quantized 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 KellanF89/phi4-cybersecurity-quantized
# Run inference directly in the terminal:
llama cli -hf KellanF89/phi4-cybersecurity-quantized Install from WinGet (Windows) winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf KellanF89/phi4-cybersecurity-quantized
# Run inference directly in the terminal:
llama cli -hf KellanF89/phi4-cybersecurity-quantized 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 KellanF89/phi4-cybersecurity-quantized
# Run inference directly in the terminal:
./llama-cli -hf KellanF89/phi4-cybersecurity-quantized 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 KellanF89/phi4-cybersecurity-quantized
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KellanF89/phi4-cybersecurity-quantized Use Docker docker model run hf.co/KellanF89/phi4-cybersecurity-quantized LM Studio Jan Ollama How to use KellanF89/phi4-cybersecurity-quantized with Ollama:
ollama run hf.co/KellanF89/phi4-cybersecurity-quantized Unsloth Studio How to use KellanF89/phi4-cybersecurity-quantized 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 KellanF89/phi4-cybersecurity-quantized 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 KellanF89/phi4-cybersecurity-quantized to start chatting Using HuggingFace Spaces for Unsloth # No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for KellanF89/phi4-cybersecurity-quantized to start chatting Pi How to use KellanF89/phi4-cybersecurity-quantized with Pi:
Start the llama.cpp server # Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KellanF89/phi4-cybersecurity-quantized 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": "KellanF89/phi4-cybersecurity-quantized"
}
]
}
}
} Run Pi # Start Pi in your project directory:
pi Hermes Agent new How to use KellanF89/phi4-cybersecurity-quantized with Hermes Agent:
Start the llama.cpp server # Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KellanF89/phi4-cybersecurity-quantized 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 KellanF89/phi4-cybersecurity-quantized Run Hermes hermes Atomic Chat new OpenClaw new How to use KellanF89/phi4-cybersecurity-quantized with OpenClaw:
Start the llama.cpp server # Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KellanF89/phi4-cybersecurity-quantized 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 "KellanF89/phi4-cybersecurity-quantized" \
--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" Docker Model Runner How to use KellanF89/phi4-cybersecurity-quantized with Docker Model Runner:
docker model run hf.co/KellanF89/phi4-cybersecurity-quantized Lemonade How to use KellanF89/phi4-cybersecurity-quantized with Lemonade:
Pull the model # Download Lemonade from https://lemonade-server.ai/
lemonade pull KellanF89/phi4-cybersecurity-quantized Run and chat with the model lemonade run user.phi4-cybersecurity-quantized-{{QUANT_TAG}} List all available models lemonade list 
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp# Start a local OpenAI-compatible server: llama serve -hf KellanF89/phi4-cybersecurity-quantized