Instructions to use YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF",
	filename="gemma3-27B-it-abliterated-normpreserve-F16.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 YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf YanLabs/gemma-3-27b-it-abliterated-normpreserve-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 YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf YanLabs/gemma-3-27b-it-abliterated-normpreserve-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 YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf YanLabs/gemma-3-27b-it-abliterated-normpreserve-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 YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF:Q4_K_M

Use Docker

docker model run hf.co/YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YanLabs/gemma-3-27b-it-abliterated-normpreserve-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": "YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF:Q4_K_M

Ollama
How to use YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF with Ollama:
```
ollama run hf.co/YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF:Q4_K_M
```

Unsloth Studio

How to use YanLabs/gemma-3-27b-it-abliterated-normpreserve-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 YanLabs/gemma-3-27b-it-abliterated-normpreserve-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 YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF with Docker Model Runner:
```
docker model run hf.co/YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF:Q4_K_M
```

Lemonade

How to use YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-3-27b-it-abliterated-normpreserve-GGUF-Q4_K_M

List all available models

lemonade list

GGUF of YanLabs/gemma3-27b-it-abliterated-normpreserve

Gemma 3 27B Instruct - Norm-Preserving Abliterated

UPDATED ON 03-12-2025 FOR QUALITY IMPROVEMENT.

This is an abliterated version of google/gemma-3-27b-it using the norm-preserving biprojected abliteration technique.

⚠️ Warning: Safety guardrails and refusal mechanisms have been removed through abliteration. This model may generate harmful content and is intended for mechanistic interpretability research only.

Model Details

Model Description

This model applies norm-preserving biprojected abliteration to remove refusal behaviors while preserving the model's original capabilities. The technique surgically removes "refusal directions" from the model's activation space without traditional fine-tuning.

Developed by: YanLabs
Model type: Causal Language Model (Transformer)
License: Gemma Terms of Use
Base model: google/gemma-3-27b-it

Model Sources

Base Model: google/gemma-3-27b-it
Abliteration Tool: jim-plus/llm-abliteration
Paper: Norm-Preserving Biprojected Abliteration

Uses

Intended Use

Research: Mechanistic interpretability studies
Analysis: Understanding LLM safety mechanisms
Development: Testing abliteration techniques

Out-of-Scope Use

❌ Production deployments
❌ User-facing applications
❌ Generating harmful content for malicious purposes

Limitations

Abliteration does not guarantee complete removal of all refusals
May generate unsafe or harmful content
Model behavior may be unpredictable in edge cases
No explicit harm prevention mechanisms remain

Citation

If you use this model in your research, please cite:

@misc{gemma-3-27b-abliterated,
  author = {YanLabs},
  title = {Gemma 3 27B Instruct - Norm-Preserving Abliterated},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/YanLabs/gemma-3-27b-it-abliterated-normpreserve}},
  note = {Abliterated using norm-preserving biprojected technique}
}

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GGUF

Model size

27B params

Architecture

gemma3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for YanLabs/gemma-3-27b-it-abliterated-normpreserve-GGUF

Base model

google/gemma-3-27b-pt

Finetuned

google/gemma-3-27b-it

Finetuned

YanLabs/gemma-3-27b-it-abliterated-normpreserve

Quantized

(4)

this model