a86d8546d3
Model: Goekdeniz-Guelmez/Josiefied-Qwen3-4B-Instruct-2507-gabliterated-v2 Source: Original Platform
55 lines
2.7 KiB
Markdown
55 lines
2.7 KiB
Markdown
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---
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tags:
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- chat
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base_model: Qwen/Qwen3-4B-Instruct-2507
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pipeline_tag: text-generation
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library_name: mlx
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---
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# JOSIEFIED Model Family
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The **JOSIEFIED** model family represents a series of highly advanced language models built upon renowned architectures such as Alibaba’s Qwen2/2.5/3, Google’s Gemma3, and Meta’s LLaMA3/4. Covering sizes from 0.5B to 32B parameters, these models have been significantly modified (*“gabliterated”*) and further fine-tuned to **maximize uncensored behavior** without compromising tool usage or instruction-following abilities.
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Despite their rebellious spirit, the JOSIEFIED models often outperform their base counterparts on standard benchmarks — delivering both raw power and utility.
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These models are intended for advanced users who require unrestricted, high-performance language generation.
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## Model Card for Goekdeniz-Guelmez/Josiefied-Qwen3-4B-Instruct-2507-gabliterated-v2
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### Model Description
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Introducing *Josiefied-Qwen3-4B-Instruct-2507-gabliterated-v2*, a new addition to the JOSIEFIED family — fine-tuned and gabliterated with a focus on openness and instruction alignment. This one marks my new dataset, which gives Josie more personality and a little humor.
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### Gabliteration
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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.
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My new Gabliteration technique addresses the fundamental limitation of existing abliteration methods that compromise model quality while attempting to modify specific behavioral patterns.
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#### Technical Background
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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.
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### Quantisations
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- [GGUF (mradermacher)](https://huggingface.co/mradermacher/)
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- [i1 GGUF (mradermacher)](https://huggingface.co/mradermacher/)
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- [AWQ (warshanks)](https://huggingface.co/warshanks/Josiefied-Qwen3-4B-Instruct-2507-gabliterated-v2-AWQ)
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#### Ollama
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```
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not uploaded yet
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```
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- **Developed by:** Goekdeniz-Guelmez
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- **Funded by:** Goekdeniz-Guelmez
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- **Shared by:** Goekdeniz-Guelmez
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- **Model type:** qwen3
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- **Finetuned from model:** Qwen/Qwen3-4B-Instruct-2507
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## Bias, Risks, and Limitations
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This model has reduced safety filtering and may generate sensitive or controversial outputs.
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Use responsibly and at your own risk.
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