Instructions to use ccharnkij/Phi-4-Uncensored-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ccharnkij/Phi-4-Uncensored-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ccharnkij/Phi-4-Uncensored-GGUF",
	filename="Phi-4-Uncensored-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ccharnkij/Phi-4-Uncensored-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ccharnkij/Phi-4-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ccharnkij/Phi-4-Uncensored-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 ccharnkij/Phi-4-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ccharnkij/Phi-4-Uncensored-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 ccharnkij/Phi-4-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ccharnkij/Phi-4-Uncensored-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 ccharnkij/Phi-4-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ccharnkij/Phi-4-Uncensored-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ccharnkij/Phi-4-Uncensored-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ccharnkij/Phi-4-Uncensored-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ccharnkij/Phi-4-Uncensored-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": "ccharnkij/Phi-4-Uncensored-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ccharnkij/Phi-4-Uncensored-GGUF:Q4_K_M

Ollama
How to use ccharnkij/Phi-4-Uncensored-GGUF with Ollama:
```
ollama run hf.co/ccharnkij/Phi-4-Uncensored-GGUF:Q4_K_M
```

Unsloth Studio

How to use ccharnkij/Phi-4-Uncensored-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 ccharnkij/Phi-4-Uncensored-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 ccharnkij/Phi-4-Uncensored-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ccharnkij/Phi-4-Uncensored-GGUF to start chatting

Docker Model Runner
How to use ccharnkij/Phi-4-Uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/ccharnkij/Phi-4-Uncensored-GGUF:Q4_K_M
```

Lemonade

How to use ccharnkij/Phi-4-Uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ccharnkij/Phi-4-Uncensored-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Phi-4-Uncensored-GGUF-Q4_K_M

List all available models

lemonade list

Phi-4-Uncensored-GGUF

GGUF quantized versions of Phi-4-Uncensored, a fine-tuned version of phi-4 with uncensored responses.

Model Details

Base Model: microsoft/phi-4
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Model Format: GGUF (text only)
License: MIT

What's Different

Removed refusal behaviors and safety filters
Capable of generating adult content
Responds to prompts the base model would typically decline
Retains the general knowledge and instruction-following capability of Phi-4

ℹ️ Notes on Uncensoring

This model is zero-shot uncensored but not 100% guaranteed. Occasionally the model may still produce a refusal depending on the prompt. If this happens, simply regenerate — it should comply on retry.

Recommended Parameters

These are the parameters used during testing for best results:

Parameter	Value
Temperature	0.85
Top-K	40
Top-P	0.5

💬 Feedback

If you use this model, I'd love to hear from you! Feel free to:

Leave a comment on this model page
Open a discussion for questions or suggestions
Report any issues or unexpected behaviors

Your feedback helps improve future versions.

⚠️ Disclaimer

This model is intended for research and educational purposes only.

This model is provided as-is, for adults only (18+).

License

This model is licensed under the MIT License.

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GGUF

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phi3

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