Anchor-Assisted Post-Hoc Hybrid Quantization of Qwen 2.5 14B: Skip-Ablation-Guided b1.58 / 4-bit Layer Interleaving for Residual Stream Resynchronization Without QAT Layer-wise quantization sensitivity in pre-trained transformers is non-uniform and partially predictable from skip-ablation data. Layers that tolerate removal also tolerate aggressive quantization; layers that are catastrophic to remove must retain higher precision. By interleaving low-precision (b1.58 ternary) layers at skip-tolerant positions with higher-precision (4-bit) anchor layers at skip-critical positions, the residual stream resynchronizes between low-precision blocks — the anchor layers absorb and correct accumulated approximation drift before it compounds into runaway error. This permits post-hoc conversion of pre-trained weights to a heterogeneous precision layout without quantization-aware training, preserving perplexity within tolerance of a uniform 4-bit baseline while reducing memory footprint below it. The theory rests on four stacked claims, each independently falsifiable: Sensitivity is non-uniform. Transformer layers contribute unequally to output quality; some are removable with modest degradation, others catastrophic to lose. Skip-tolerance transfers to quantization-tolerance. Layers that survive removal survive heavy quantization. Skip and quantize are different perturbations (absence vs. active noise injection), so this transfer is assumed, not proven. Anchors resynchronize the residual stream. Consecutive low-precision layers compound error in the residual stream. Higher-precision layers interleaved between them have enough headroom to absorb drift and prevent runaway divergence. Post-hoc conversion is viable without QAT. Pre-trained weights, reassigned to mixed precisions in this pattern, retain enough learned function to operate. This is the most speculative claim — b1.58 was designed for from-scratch training, and post-hoc conversion is unsolved. Failure of any single claim collapses the result, but each failure mode is informative about which mechanism in the stack actually drives transformer robustness. https://preview.redd.it/9q65xmwj65yg1.jpg?width=749&format=pjpg&auto=webp&s=cda80d441c96432a7287a3cb6adc0ed34cc5d216 submitted by /u/BLOCK__HEAD4243
Originally posted by u/BLOCK__HEAD4243 on r/ArtificialInteligence