Instructions to use Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF", dtype="auto")

llama-cpp-python

How to use Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF",
	filename="gemma-4-E2B-it-qat-unquantized-abliterated-Q4_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-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 Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF:BF16

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 Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF:BF16

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 Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF:BF16

Use Docker

docker model run hf.co/Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF:BF16

LM Studio
Jan
Ollama
How to use Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF with Ollama:
```
ollama run hf.co/Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF:BF16
```

Unsloth Studio

How to use Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-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 Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-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 Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF to start chatting

How to use Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF:BF16

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": "Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-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 Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF:BF16

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 Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF:BF16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF:BF16
```

Lemonade

How to use Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF:BF16

Run and chat with the model

lemonade run user.Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF-BF16

List all available models

lemonade list

huihui-ai/Huihui-gemma-4-E2B-it-qat-q4_0-unquantized-abliterated-GGUF

This is an uncensored version of google/gemma-4-E2B-it-qat-q4_0-unquantized created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.

ollama

Please use the latest version of ollama

You can use huihui_ai/gemma-4-abliterated:E2B-qat directly,

ollama run huihui_ai/gemma-4-abliterated:E2B-qat

GGUF

Please use the latest version of ggml-org/llama.cpp

apt-get update
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build \
    -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --clean-first --target llama-cli llama-mtmd-cli llama-server llama-gguf-split
cp llama.cpp/build/bin/llama-* llama.cpp

./llama.cpp/llama-cli \
    -m huihui-ai/Huihui-gemma-4-E2B-it-qat-q4_0-unquantized-abliterated-GGUF/Huihui-gemma-4-E2B-it-qat-q4_0-unquantized-abliterated-Q4_K.gguf \
    -ngl 99 -c 262144 \
    --spec-type draft-mtp \
    --spec-draft-n-max 4 \
    --spec-draft-model huihui-ai/Huihui-gemma-4-E2B-it-qat-q4_0-unquantized-abliterated-GGUF/mtp-ggml-model-bf16.gguf

Usage Warnings

Risk of Sensitive or Controversial Outputs: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.
Not Suitable for All Audiences: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.
Legal and Ethical Responsibilities: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.
Research and Experimental Use: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.
Monitoring and Review Recommendations: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.
No Default Safety Guarantees: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.

Donation

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Downloads last month: 3

GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

16-bit

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Any-to-Any

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Model tree for Dzluck/Gemma-4-E2B-it-qat-unquantized-abliterated-GGUF

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Finetuned

google/gemma-4-E2B-it-qat-q4_0-unquantized

Quantized

(28)

this model