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", device_map="auto")

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

phi4-mini-intent-GGUF / README.md

cngchis

Update README.md

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	---
	license: mit
	datasets:
	- cngchis/Support-Ticket-Router-12K-Cleaned
	language:
	- en
	metrics:
	- f1
	- confusion_matrix
	- precision
	- recall
	- accuracy
	base_model:
	- unsloth/Phi-4-mini-instruct
	new_version: cngchis/phi4-mini-intent
	pipeline_tag: text-classification
	library_name: transformers
	tags:
	- phi
	- nlp
	- intent
	- classification
	- math
	- code
	- finance
	---

	## 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`:

	```bash
	./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.