初始化项目，由ModelHub XC社区提供模型

Model: Goekdeniz-Guelmez/Qwen2.5-3B-gabliterated-Dev
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-7B-instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- uncensored
+- code
+- legal
+- text-generation-inference
+---
+
+# 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.
+
+## 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/)
+- [i1 GGUF (mradermacher)](https://huggingface.co/mradermacher/)
+
+## 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.