NSFW

Uncensored

Decensored

Heretic

ARA

Model card Files Files and versions

xet

Community

Instructions to use Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

llama-cpp-python

How to use Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF",
	filename="Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-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 Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-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 Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-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 Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF with Ollama:
```
ollama run hf.co/Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-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 Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-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 Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF to start chatting

Pi new

How to use Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-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": "Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-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 Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-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 Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF with Docker Model Runner:
```
docker model run hf.co/Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF:Q4_K_M
```

Lemonade

How to use Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF-Q4_K_M

List all available models

lemonade list

After thorough usage and testing, I chose two UNCENSORED Gemma 4 E4B Models to be pruned (converted to English and Text-to-Text only) so I can use them on my potato laptop/phone.

The Source Models

1. Gemma-4-E4B-it-uncensored

Profile: The most uncensored G4-E4B model.
Stats: 0/656 manually checked refusals (according to the author). KL divergence value of 0.068 (very negligible).

2. Gemma-4-E4B-it-ultra-uncensored-heretic (Base for this pruned version)

Profile: The most intelligent uncensored G4-E4B model with the lowest KL divergence on Hugging Face.
Stats: KL divergence of 0.0076. Refusals 3/100 (which you probably won't notice anyways).

This pruned model is the more intelligent one, while still being really heavily uncensored.

Check out the other one here: Gemma-4-E4B-it-uncensored-pruned-Text-and-English-ONLY-GGUF.

Modifications

What's Changed:

Vision & Audio Modules: REMOVED (Just Text-to-Text now).
Language Tokens: ~61,000 (23.1%) REMOVED This includes non-Latin, Cyrillic, Arabic, Asian languages, etc.
- Note: Languages using some Latin letters like German and Vietnamese are still there. Latin was not removed to keep the logic alive.
Weights: Cut down from >2300 to 720 (loads fast as fahhhh).

What DID NOT Change:

Layers
Logic (as far as I tested)
Context
No training/retraining done

TL;DR: Overall, both pruned models are faster and significantly less resource-intensive.

Base Model Usage Settings

You might want to change these according to your preferences, but here is the baseline:

Temperature: 1.0
Top P: 0.95
Top K: 64
Jinja --jinja (don't ignore this one, especially on llama.cpp!)

Colab Notebook I made and used to prune these models:

Gemma-4-E4B Pruner

Thanks:

InfinimindCreations
llmfan46
Claude.ai (for coding the notebook)
Kaggle.com (for the free resources)

These were my first attempts at anything to do with modifying a model so if you wanna suggest something, do it.

Downloads last month: 1,081

GGUF

Model size

7B params

Architecture

gemma4

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

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Model tree for Handyfff/Gemma-4-E4B-it-ultra-uncensored-heretic-pruned-TextOnly-EnglishOnly-GGUF

Base model

google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Finetuned

llmfan46/gemma-4-E4B-it-ultra-uncensored-heretic

Quantized

(14)

this model