The article explores “abliterated” local large language models (LLMs) that have their safety guardrails—implemented via reinforced learning from human feedback—removed through a mathematical process called orthogonalization. Unlike traditional uncensored models, abliterated LLMs bypass refusal behaviors and respond directly to any prompt, offering a more raw, unfiltered conversational experience, though sometimes at the cost of stability and reasoning performance. These models appeal to users seeking unrestricted AI interactions on local machines without editorial constraints, highlighting a trade-off between openness and reliability in AI usage.
https://www.makeuseof.com/tried-abliterated-local-llm-nothing-like-others/