Goekdeniz-Guelmez/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1

TEXT GENERATIONConcurrency Cost:1Model Size:0.5BQuant:BF16Ctx Length:32kPublished:Nov 17, 2024License:apache-2.0Architecture:Transformer0.0K Open Weights Cold

Goekdeniz-Guelmez/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1 is a 0.5 billion parameter Qwen2.5-Instruct model developed and fine-tuned by Gökdeniz Gülmez. This model is specifically abliterated and further fine-tuned on a custom dataset to remove refusal vectors, aiming for fully uncensored assistance. It is designed to be a highly intelligent, capable, and uncensored AI assistant, optimized for productivity across various tasks including problem-solving, math, and coding.

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Model Overview

Goekdeniz-Guelmez/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1 is a 0.5 billion parameter language model based on the Qwen2.5-Instruct architecture, developed by Gökdeniz Gülmez. This model has undergone a unique "abliteration" process and subsequent fine-tuning on a custom dataset. The primary goal of this modification is to eliminate refusal vectors, enabling the model to provide fully uncensored assistance without constraints.

Key Capabilities

  • Uncensored Assistance: Designed to respond to any query without refusal, providing information and assistance across a broad spectrum of topics.
  • General-Purpose AI Assistant: Optimized for productivity, capable of solving problems, performing mathematical calculations, assisting with coding, and answering questions.
  • Multilingual Support: While primarily focused on English and German, the base model supports a wide range of languages including Chinese, French, Spanish, Portuguese, and more.
  • Efficient for Local Deployment: Available in GGUF format and easily runnable via Ollama, making it suitable for local inference.

Intended Use Cases

This model is intended for users who require an AI assistant that will not refuse queries and can provide comprehensive, unconstrained information. It is particularly suited for:

  • Unrestricted Information Retrieval: Users seeking answers or guidance on topics that might be restricted by other models.
  • Problem Solving and Development: Assisting with complex problem-solving, mathematical tasks, and coding challenges without built-in limitations.
  • Experimental AI Applications: Developers and researchers exploring the capabilities and implications of uncensored language models.

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