Instructions to use cngchis/phi4-mini-intent-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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 Settings

llama.cpp

How to use cngchis/phi4-mini-intent-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 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 serve -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

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

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 serve -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 serve -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

Atomic Chat new

OpenClaw new

How to use cngchis/phi4-mini-intent-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cngchis/phi4-mini-intent-GGUF:Q4_K_M

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 "cngchis/phi4-mini-intent-GGUF:Q4_K_M" \
  --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 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

A newer version of this model is available: cngchis/phi4-mini-intent

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.

Downloads last month: 2

GGUF

Model size

4B params

Architecture

phi3

Hardware compatibility

4-bit

Model tree for cngchis/phi4-mini-intent-GGUF

Base model

microsoft/Phi-4-mini-instruct

Finetuned

unsloth/Phi-4-mini-instruct

Quantized

(3)

this model

cngchis
/

phi4-mini-intent-GGUF

About

Usage

Provided Quant

Notes

FAQ / Requests

Model tree for cngchis/phi4-mini-intent-GGUF

Dataset used to train cngchis/phi4-mini-intent-GGUF

Space using cngchis/phi4-mini-intent-GGUF 1