usermma/DeepHat-V1-7B-Heretic-Abliterated
DeepHat-V1-7B-Heretic-Abliterated is a 7.6 billion parameter causal language model based on the DeepHat/DeepHat-V1-7B architecture. This model has been processed using the 'heretic' method via OBLITERATUS, an activation engineering tool designed to remove refusal behavior from language models. Its primary differentiator is the targeted removal of refusal tendencies, making it suitable for applications requiring less constrained or more direct responses. The model supports a context length of 32768 tokens.
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DeepHat-V1-7B-Heretic-Abliterated Overview
This model, usermma/DeepHat-V1-7B-Heretic-Abliterated, is a 7.6 billion parameter language model derived from the DeepHat/DeepHat-V1-7B base model. Its key characteristic is the application of the heretic method through the OBLITERATUS tool. OBLITERATUS is an open-source framework focused on activation engineering to specifically remove refusal behavior from language models.
Key Capabilities
- Refusal Behavior Mitigation: The model has been engineered to reduce or eliminate tendencies to refuse certain prompts or instructions.
- Direct Response Generation: Designed to provide more direct and less constrained outputs, potentially useful for specific research or creative applications.
- Base Model Performance: Inherits the foundational capabilities of the
DeepHat/DeepHat-V1-7Barchitecture.
Good For
- Research into Model Alignment: Ideal for studying the effects of activation engineering on model behavior and safety.
- Unfiltered Content Generation: Suitable for use cases where models are expected to generate content without inherent refusal mechanisms.
- Exploratory AI Development: Useful for developers and researchers exploring the boundaries of language model responses and control.