Unverified50% confidenceDecision RuleExact time
The author does not default every LoRA to a strength of 1.0; after testing, the recommended strength for most models ultimately falls in the 0.8–1.2 range.
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50%
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Long-term
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9/24/2026
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VerifiedWhen fine-tuning with LoRA, the actual number of parameters that need to be trained is only 0.1%-1% of the original.68% similarUnverifiedThe KLD of models uncensored via traditional full fine-tuning typically falls in the range of 0.1 to 0.5 or even higher, while LoRA-based methods generally fall between 0.05 and 0.15.67% similarUnverifiedThe core of evaluation is scoring, typically expressing the quality of results using a continuous value from 0 to 1.67% similarUnverifiedThe MTP head parameter count is typically less than 1% of the main model and is jointly pre-trained with the main model.66% similarUnverifiedThe trained model achieves a per-token negative log-likelihood (NLL) of 0.93, compared to 12.60 for a randomly initialized twin model of the same architecture.66% similar
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