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---
license: apache-2.0
library_name: rkllm
tags:
- rkllm
- rockchip
- rk3588
- qwen3
- uncensored
- code
- legal
- text-generation-inference
base_model_relation: quantized
base_model:
- Goekdeniz-Guelmez/Gabliterated-Qwen3-0.6B
---
# Gabliterated Model Series
## 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.
## 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.
## 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.
## Citation
If you use these models, please cite the original research (paper comming later this year):
```
Gülmez, G. (2025). Gabliteration: Adaptive Multi-Directional Neural Weight Modification for Selective Behavioral Alteration in Large Language Models.
```
## Acknowledgments
This work builds upon the foundational research by Arditi et al. (2024) on refusal direction identification in large language models.
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--- |