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@teortaxesTex

Unpopular take: LLM community is *coping* about quantization. Any real test of reasoning shows k-quants≤Q5 fail. Ppl, evals are misleading: do you care that it's only 1% loss if it takes 99% of the hardest skills? We need kernels for AWQ, SpQR, or better – for all platforms. https://t.co/5RNOScIzgj

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  "full_text": "Unpopular take: LLM community is *coping* about quantization. Any real test of reasoning shows k-quants≤Q5 fail. Ppl, evals are misleading: do you care that it's only 1% loss if it takes 99% of the hardest skills? We need kernels for AWQ, SpQR, or better – for all platforms. https://t.co/5RNOScIzgj",
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    "full_text": "They are hinting at that, sure.\nBut they're testing on OPT, as in most of those Hype-Aware Quantization papers\nWhy?\nOPT's FF layers use ReLU. It sacrifices perplexity but makes activations sparse.\nI'm skeptical it'll work for SwiGLU in LLaMA… without retrain.\n(paper:MoEfication) https://t.co/LbkvP5LW5i",
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