Instructions to use cngchis/phi4-mini-intent-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cngchis/phi4-mini-intent-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cngchis/phi4-mini-intent-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cngchis/phi4-mini-intent-GGUF", dtype="auto") - llama-cpp-python
How to use cngchis/phi4-mini-intent-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="cngchis/phi4-mini-intent-GGUF", filename="phi-4-mini-intent-q4_k_m.gguf", )
llm.create_chat_completion( messages = "\"I like you. I love you\"" )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use cngchis/phi4-mini-intent-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf cngchis/phi4-mini-intent-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf cngchis/phi4-mini-intent-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 cngchis/phi4-mini-intent-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf cngchis/phi4-mini-intent-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 cngchis/phi4-mini-intent-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf cngchis/phi4-mini-intent-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 cngchis/phi4-mini-intent-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf cngchis/phi4-mini-intent-GGUF:Q4_K_M
Use Docker
docker model run hf.co/cngchis/phi4-mini-intent-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use cngchis/phi4-mini-intent-GGUF with Ollama:
ollama run hf.co/cngchis/phi4-mini-intent-GGUF:Q4_K_M
- Unsloth Studio new
How to use cngchis/phi4-mini-intent-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 cngchis/phi4-mini-intent-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 cngchis/phi4-mini-intent-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cngchis/phi4-mini-intent-GGUF to start chatting
- Pi new
How to use cngchis/phi4-mini-intent-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf cngchis/phi4-mini-intent-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": "cngchis/phi4-mini-intent-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use cngchis/phi4-mini-intent-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 cngchis/phi4-mini-intent-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 cngchis/phi4-mini-intent-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use cngchis/phi4-mini-intent-GGUF with Docker Model Runner:
docker model run hf.co/cngchis/phi4-mini-intent-GGUF:Q4_K_M
- Lemonade
How to use cngchis/phi4-mini-intent-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cngchis/phi4-mini-intent-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.phi4-mini-intent-GGUF-Q4_K_M
List all available models
lemonade list
About
Static GGUF quantization for an Intent Classification model.
This model is converted from a Hugging Face checkpoint and optimized for local inference using llama.cpp compatible runtimes.
The model is designed for predicting intent labels from user input text.
Usage
If you are unsure how to use GGUF files, refer to: https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF
Basic usage with llama.cpp:
./main -m model.Q4_K_M.gguf -p "Your input text here"
For classification tasks, ensure your prompt format matches the training setup (e.g., instruction or label format).
Provided Quant
| Link | Type | Size/GB | Notes |
|---|---|---|---|
| GGUF | Q4_K_M ~X.X | 2.5 | recommended balance of speed and quality |
Notes
- This model is fine-tuned for intent classification tasks only
- Best performance when input follows the same format as training data
- Q4_K_M provides a good trade-off between accuracy and inference speed
FAQ / Requests
Thanks
Thanks to the open-source GGUF ecosystem (llama.cpp, ggml) and Hugging Face community.
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Model tree for cngchis/phi4-mini-intent-GGUF
Base model
microsoft/Phi-4-mini-instruct