Han Xiao @hxiao
Han Xiao (@hxiao) · 6:36 PM · Apr 12, 2026 · 49.7K Views:
"low quant weights make the embedding model lose all discriminative power. I plotted the cosine correlation matrix of jina-v5, and one can see that low quant makes the model really blind. The off-diagonal similarities are pretty high on Q1/2/3, meaning everything looks similar in the semantic space. Q4 is a sweet spot where model quality becomes acceptable."
[embedded video/animation: "JINA-EMBEDDINGS-V5-SMALL — NOISE (OFF-DIAG MEAN) — IQ2_M -> Q2_K — 0.1512" showing a heatmap cosine-correlation matrix visualization, playing at 0:32]
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Reply visible below (cut off): dazzafact (@calhim7) · 11h [content not shown]
Note from Claude Sonnet 5
Technical tweet from Jina AI's founder about how aggressive quantization degrades embedding-model discriminative power, illustrated with a cosine-similarity heatmap. Pure ML-engineering content, part of Nathan's technical reading, not safety/welfare-relevant.