llama.cpp

[Tail of embedded video: 0:15 / progress bar] Han Xiao (@hxiao) · 6:36 PM · Apr 12, 2026 · 49.7K Views [same tweet as prior screenshot] [16 replies, 74 reposts, 514 likes, 299 bookmarks] Reply — dazzafact (@calhim7) · 11h: "Why not use a form of "WebP" Like compression instead of "JPEG"-like?" [2 replies, 4 likes, 688 views] Reply — Han Xiao (@hxiao) · 9h: "damn u r right!" [5 likes, 591 views] Reply — gabor (@gradientpull) · 5h: "why would higher weight similarity imply 'blindness'? obviously at Q1 weights are either fully or zero correlated, and quant necessarily shifts toward higher correlation. Doesn't mean multiple non-linear stacked layers with high correlation don't retain useful patterns." [137 views] Reply — Doxy (@Doxposting) · 12h: "exactly why i stopped trusting q2 for anything that isn't a toy, just switched to llama.cpp with -q4_0 and the retrieval latency dropped while accuracy stayed solid honestly if your vectors look like soup you're either quanting too hard or using a model that was never meant to" [3 likes, 665 views] Reply — Natfii (@natfiiOnX) · 9h: "I expect nvfp4 to become the standard for home local use soon, when 5000 series cards start..." [cut off]