QM-ToT applies Tree of Thoughts decomposition and evaluator layers to quantized LLMs, reporting accuracy gains from 34% to 50% on MedQAUSMLE for LLaMA2-70b and from 58.77% to 69.49% for LLaMA-3.1-8b, plus an 86.27% improvement in data distillation using only 3.9% of the data.
Seed-cts: Unleashing the power of tree search for superior performance in competitive coding tasks,
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QM-ToT: A Medical Tree of Thoughts Reasoning Framework for Quantized Model
QM-ToT applies Tree of Thoughts decomposition and evaluator layers to quantized LLMs, reporting accuracy gains from 34% to 50% on MedQAUSMLE for LLaMA2-70b and from 58.77% to 69.49% for LLaMA-3.1-8b, plus an 86.27% improvement in data distillation using only 3.9% of the data.