venkycs/Mistral-Small-24B-Instruct-2501-Abliterated

Hugging Face
TEXT GENERATIONConcurrent Unit Cost:2Model Size:24BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jan 30, 2025License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Warm

venkycs/Mistral-Small-24B-Instruct-2501-Abliterated is an uncensored version of the Mistral-Small-24B-Instruct-2501 model, created by huihui-ai using an abliteration technique. This model is a proof-of-concept implementation designed to remove refusal behaviors from the original LLM without relying on TransformerLens. It is intended for use cases where an instruction-tuned model with reduced censorship is desired.

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Model Overview

This model, venkycs/Mistral-Small-24B-Instruct-2501-Abliterated, is an uncensored variant of the mistralai/Mistral-Small-24B-Instruct-2501 instruction-tuned language model. It was developed by huihui-ai using a technique called "abliteration," which aims to remove refusal mechanisms present in the base model.

Key Characteristics

  • Uncensored Behavior: The primary modification is the removal of refusal responses, making it suitable for applications requiring less restrictive output.
  • Proof-of-Concept: This implementation serves as a demonstration of how to modify LLM behavior to reduce refusals without using advanced tools like TransformerLens.
  • Base Model: Built upon the Mistral-Small-24B-Instruct-2501 architecture, inheriting its general capabilities as an instruction-following model.

Usage

This model can be easily integrated into workflows using Ollama. A pre-packaged version, huihui_ai/mistral-small-abliterated, is available for direct use with the ollama run command.

Intended Use Cases

This model is particularly useful for developers and researchers exploring methods of controlling LLM output and reducing inherent refusal behaviors. It is designed for scenarios where a more direct and less constrained response from an instruction-tuned model is preferred, serving as a foundational experiment in model modification.