Gemma-2-2B legal/financial SFT -- RQ-compressed embedding
Same weights as prashanthsura/gemma-2-2b-legal-financial-sft except the tied token-embedding matrix (256000 x 2304, ~590M params, ~22.6% of the model) has been replaced by its 2-stage Residual Product Quantization reconstruction (M=384 subspaces, K=1024 codewords per stage, L=2 stages -> 3.59 bits/param for that matrix vs 16 in bf16). Because Gemma ties its output head to the embedding, one codebook covers both roles ("unified codebook").
The main model files store the reconstructed embedding for drop-in
from_pretrained loading. The genuinely compressed artifact (int16 codes +
fp32 codebooks) is in rq_M384_K1024_artifact.pt.
Demo + head-to-head evaluation: https://prashanthsura-gemma-2-2b-rq.vercel.app