A comprehensive head-to-head comparison of 32 local large language models (LLMs) on a fact-extraction corpus revealed that most models performed similarly. The study, which utilized paired bootstrap testing on consumer-grade graphics cards, found that only the top two models showed a discernible difference, with a 35B MoE model narrowly outperforming a dense 27B model from the same family. Notably, the recently released LFM2.5 models performed unexpectedly poorly, scoring lower than much smaller models, suggesting potential issues with the new models or the testing methodology. AI
IMPACT Provides insights into the relative performance of various local LLMs, aiding users in selecting models for specific tasks.
RANK_REASON Research paper detailing comparative performance of multiple LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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