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Google's LLM Quantization Process Found to Be Broken

A Reddit user has identified issues with Google's quantization process for large language models, specifically noting that the llama-quantize function is hardcoded incorrectly and misaligns block groups. The user suggests that the unsloth Q4_K_XL quantization method is a more reliable alternative for now. A patch is reportedly in development to address these quantization errors. AI

IMPACT Highlights potential issues in LLM quantization tools, impacting model efficiency and performance.

RANK_REASON User-identified technical issue with an open-source tool related to LLM quantization. [lever_c_demoted from research: ic=1 ai=0.7]

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/dreamkast06 ·

    Quick note on the QAT of recent

    <!-- SC_OFF --><div class="md"><p>tldr: Googles quant is broken, use unsloth UD Q4_K_XL for now</p> <p>This might be low quality post, but oh well, we ball</p> <p>llama-quantize will quant the token embed to q6k when Google really was supposed to use &quot;--pure&quot; but that’s…