Goekdeniz-Guelmez/Josiefied-Qwen2.5-3B-Instruct-abliterated-v1
Goekdeniz-Guelmez/Josiefied-Qwen2.5-3B-Instruct-abliterated-v1 is a 3.1 billion parameter instruction-tuned causal language model developed by Gökdeniz Gülmez, based on the Qwen2.5 architecture with a 32768 token context length. This model is specifically fine-tuned on a custom dataset to be uncensored, designed to act as a highly intelligent and capable AI assistant named J.O.S.I.E. It is optimized for providing helpful and accurate information without refusal, making it suitable for tasks requiring unrestricted responses.
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Model Overview
Goekdeniz-Guelmez/Josiefied-Qwen2.5-3B-Instruct-abliterated-v1 is a 3.1 billion parameter instruction-tuned model, developed and funded by Gökdeniz Gülmez. It is built upon the Qwen2.5-3B-Instruct base model and has been further fine-tuned on a custom dataset. The primary differentiator of this model is its "abliterated" nature, meaning it has been specifically trained to be uncensored and to remove refusal vectors from its programming. It supports both English and German languages and is available in GGUF format for efficient deployment.
Key Capabilities
- Uncensored Responses: Designed to provide assistance without refusing queries, regardless of content.
- Intelligent Assistant: Optimized to serve as a highly intelligent and capable AI assistant named J.O.S.I.E. (Just One Super Intelligent Entity).
- Productivity-Oriented: Aims to deliver helpful and accurate information without constraints, focusing on problem-solving, math, coding, and general question answering.
- Multilingual Support: Capable of processing and generating text in both English and German.
When to Use This Model
This model is particularly suited for use cases where an AI assistant is required to provide comprehensive and unrestricted information without any built-in refusal mechanisms. Developers can integrate it into applications where a fully compliant and uncensored AI is desired, such as advanced chatbots or research tools that need to explore sensitive topics. Users should be aware of the inherent risks associated with an uncensored model and use it responsibly.