Instructions to use canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use canyrtcn/Gemma_E4B_Kizagan_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 canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:F16

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 canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:F16

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 canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:F16

Use Docker

docker model run hf.co/canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:F16

LM Studio
Jan

vLLM

How to use canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "canyrtcn/Gemma_E4B_Kizagan_Abliterated-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": "canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:F16

Ollama
How to use canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF with Ollama:
```
ollama run hf.co/canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:F16
```

Unsloth Studio

How to use canyrtcn/Gemma_E4B_Kizagan_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 canyrtcn/Gemma_E4B_Kizagan_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 canyrtcn/Gemma_E4B_Kizagan_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 canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF to start chatting

How to use canyrtcn/Gemma_E4B_Kizagan_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 canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:F16

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": "canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use canyrtcn/Gemma_E4B_Kizagan_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 canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:F16

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 canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:F16

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 "canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:F16" \
  --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 canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:F16
```

Lemonade

How to use canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:F16

Run and chat with the model

lemonade run user.Gemma_E4B_Kizagan_Abliterated-GGUF-F16

List all available models

lemonade list

🔓 Gemma E4B Kızagan Abliterated — GGUF

canyrtcn/Gemma_E4B_Kizagan_Abliterated modelinin GGUF quantize edilmiş versiyonları.

📦 Bu repoda bulunan quant'lar

Dosya	Quant	Boyut	Kalite	Önerilen
`Gemma_E4B_Kizagan_Abliterated-Q4_K_M.gguf`	Q4_K_M	~5 GB	⭐⭐⭐⭐	8GB VRAM, dengeli kullanım
`Gemma_E4B_Kizagan_Abliterated-F16.gguf`	F16	~15 GB	⭐⭐⭐⭐⭐	16GB+ VRAM, maksimum kalite

🚀 Kullanım

🦙 llama.cpp

# Q4_K_M
llama-cli -hf canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:Q4_K_M \
  --chat-template gemma

# F16
llama-cli -hf canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF:F16 \
  --chat-template gemma

🐳 Ollama

ollama create kizagan-abliterated -f Modelfile

Modelfile:

FROM ./Gemma_E4B_Kizagan_Abliterated-Q4_K_M.gguf
PARAMETER temperature 0.3
PARAMETER top_p 0.95
PARAMETER top_k 20
PARAMETER repeat_penalty 1.05
TEMPLATE """{{ if .System }}<|turn>system
{{ .System }}<turn|>
{{ end }}<|turn>user
{{ .Prompt }}<turn|>
<|turn>model
"""

🖥️ LM Studio

Repoyu aratıp indirebilirsiniz.

📐 Model Hakkında

Bu GGUF'lar, canyrtcn/Gemma_E4B_Kizagan_Abliterated modelinden dönüştürülmüştür:

Temel model: Gemma 4 E4B-it (Google DeepMind)
Türkçe SFT: AlicanKiraz0/Kizagan-E4B-Turkish-Reasoning-Model (540K satır)
Abliteration: Reddetme davranışı kaldırıldı
Mimari: Gemma 4 (MoE-style PLE, 7.5B toplam / 4B efektif)
Context: 131K token

⚙️ Önerilen Parametreler

Parametre	Değer
`temperature`	0.3
`top_p`	0.95
`min_p`	0.05
`top_k`	20
`repeat_penalty`	1.05

⚠️ Uyarı

Bu modelin reddetme davranışı kaldırılmıştır (abliterated). Filtrelenmemiş içerik üretebilir. Kullanım sorumluluğu size aittir.

📜 Lisans

Apache 2.0. Google Gemma Terms of Use koşullarına tabidir.

🔗 Bağlantılar

Ana model (safetensors): canyrtcn/Gemma_E4B_Kizagan_Abliterated
Orijinal Kızagan: AlicanKiraz0/Kizagan-E4B
Temel model: google/gemma-4-E4B-it

Downloads last month: 625

GGUF

Model size

8B params

Architecture

gemma4

Hardware compatibility

4-bit

16-bit

Model tree for canyrtcn/Gemma_E4B_Kizagan_Abliterated-GGUF

Base model

google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Finetuned

AlicanKiraz0/Kizagan-E4B-Turkish-Reasoning-Model

Finetuned

canyrtcn/Gemma_E4B_Kizagan_Abliterated

Quantized

(2)

this model