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ShadowPEFT – Centralized and Detachable Parameter-Efficient Fine-Tuning
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Big BrainZero to OneNiche Gem
yokee
621mo ago
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SRT-Adapter: 0.99 AUROC, perplexity win and 16.7× recall (0.19%)
by spacebacon·Apr 26, 2026·2 points·0 comments
AI Analysis
●MidNiche GemBold Bet
Training code withheld during patent review limits reproducibility and community adoption.
Strengths
- •0.19% trainable parameters (14.5M) on frozen 7B backbone with no degradation
- •0.99 AUROC on regime detection with 16.7× recall improvement over baseline
- •Four semiotic channel readouts per token position for discourse analysis
Weaknesses
- •Training pipelines and loss code not included during patent and publication review
- •Requires custom SRTAdapter class loader, not AutoModel-loadable checkpoint
Category
Target Audience
ML researchers, NLP engineers studying model interpretability
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LoRA · QLoRA · AdapterHub
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