Qwen3.6 27B FABQ RC GGUF https://github.com/toxzak svg/fabq rc Model Description FABQ RC (Fisher Adaptive Binary Quantization with Residual Codebooks) is a 1 bit quantization method for large language models that adapts per layer rather than using a fixed blocksize. This quantization of Qwen3.6 27B achieves ~1.18 bits per parameter while maintaining quality through four key innovations. Property Value Base Model Qwen/Qwen3.6 27B Quantization Method FABQ RC Format GGUF (v3) Bits per Parameter ~1.18 bpw Precision Allocation Top 5% channels → int4, 95% → binary ±1 Blocksize Adaptive per layer {64, 128, 256, 512} Calibration Dataset C4 (2048 samples, seq len=32) How It Works FABQ RC combines four innovations: 1. Fisher Weighted Channel Importance — Uses Fisher Information (expected gradient²) to determine which channels actually matter for the loss. This is more directly relevant than Hessian (curvature) or magnitude alone. 2. Mixed Precision Core Allocation — Top 5% Fisher channels → int4 (preserve accuracy). Bottom 95% → binary ±1 (maximum compression). 3. Adaptive Blocksize — Each layer gets its own optimal blocksize from {64, 128, 256, 512}, chosen by minimizing Fisher weighted rec…
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