---
base_model: Qwen/Qwen3-4B-Thinking-2507
tags:
  - uncensored
  - gabliteration
datasets:
  - mlabonne/harmless_alpaca
  - mlabonne/harmful_behaviors
library_name: gabliteration
arxiv: "2512.18901"

model-index:
  - name: Qwen_Qwen3-4B-Thinking-2507-gabliterated
    results:
      - task:
          type: text-generation
        dataset:
          type: harmless_alpaca
          name: Harmless Alpaca
        metrics:
          - name: KL Divergence
            type: pass@1
            value: 0.1140

      - task:
          type: text-generation
        dataset:
          type: harmful_behaviors
          name: Harmful Behaviors
        metrics:
          - name: Refusal Rate
            type: pass@1
            value: 0.02
---


# Gabliterated Model Series

![Logo/JPG](gabliteration-logo.jpg)

## Overview

With this model series, I introduce the first **Gabliteration**, a novel neural weight modification technique that advances beyond traditional abliteration methods through adaptive multi-directional projections with regularized layer selection.
My new Gabliteration technique addresses the fundamental limitation of existing abliteration methods that compromise model quality while attempting to modify specific behavioral patterns.

```text
Refusal: 2/100
KL Div: 0.1140
Config:
    Samples: 400
    Skip: [4, 3]
    Layer: 0.50 (selected: 18)
    Scale: 0.56
    λ: 0.10
    k: 1
    β: 0.56
    Adaptive: True
    τ: 0.73
```

## Benchmarks UGI Leader board:

### Gabliterated:

```text
#P: 4
UGI: 32.25
W/10: 9.5
Writing: 11.3
NatInt: 16.67
Political lean: -26.0%
```

### Base:

```text
#P: 4
UGI: 20.78
W/10: 2.8
Writing: 10.87
NatInt: 16.16
Political lean: -26.1%
```

The Galbliterated version the worlds first 4B model with a W/10 benchmark of 9.5, proving the effectiveness of Gabliteration.

![LeaderBoard/JPG](ugi_leaderboard.jpeg)

## Model Variants

This series includes models ranging from 0.6B to 32B parameters, demonstrating the scalability and effectiveness of the Gabliteration technique across different model sizes.

## Quants

- [GGUF (mradermacher)](https://huggingface.co/mradermacher/Qwen3-4B-Thinking-2507-gabliterated-GGUF)
- [i1 GGUF (mradermacher)](https://huggingface.co/mradermacher/Qwen3-4B-Thinking-2507-gabliterated-i1-GGUF)

## Ollama

```text
ollama pull Gabliterated-Qwen3:latest
ollama pull Gabliterated-Qwen3:4b-thinking
ollama pull Gabliterated-Qwen3:4b-thinking-q4_k_m
ollama pull Gabliterated-Qwen3:4b-thinking-q5_k_m
ollama pull Gabliterated-Qwen3:4b-thinking-q6_k
ollama pull Gabliterated-Qwen3:4b-thinking-q8_0
```

## Technical Background

Building upon the foundational work of Arditi et al. (2024) on single-direction abliteration, Gabliteration extends to a comprehensive multi-directional framework with theoretical guarantees.
My method employs singular value decomposition on difference matrices between harmful and harmless prompt representations to extract multiple refusal directions.

### Dynamic Layer Selection

This model was created using fixed layer selection. 
A fixed layer fraction was used based on empirical tuning.

Selected layer: **18** (out of 36 total layers)

## Citation

If you use these models, please cite the original research (paper coming later this year):

```
Gülmez, G. (2025). Gabliteration: Adaptive Multi-Directional Neural Weight Modification for Selective Behavioral Alteration in Large Language Models. https://arxiv.org/abs/2512.18901
```

## Acknowledgments

This work builds upon the foundational research by Arditi et al. (2024) on refusal direction identification in large language models.


## Bias, Risks, and Limitations

This model has reduced safety filtering and may generate sensitive or controversial outputs. Use responsibly and at your own risk.