•1 min read•from Machine Learning
ExTernD: Expanded-Rank Ternary Decomposition Ternary LLM PTQ with Accuracy Approaching Any Quantization Level [P]
https://arxiv.org/pdf/2607.13511
the core idea is, we cannot have ternary PTQ with fixed matrix size, trying to do that is dead end. so i tried decomposing the matrix to 2 ternary matrices and inner diagonal scaling matrix. now that the inner rank can be arbitrarily large the accuracy can be arbiratily small. and its not that it has to be very large too i also showed that it does take only slightly more vram then current quantisation methods. the slight more vram is worth it if we abuse the ternary math.
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Tagged with
#Ternary
#PTQ
#Quantization
#Decomposition
#Matrix
#Rank
#Accuracy
#VRAM
#LLM
#Scaling
#Ternary Math
#Expanded-Rank
#Matrix Size
#Inner Diagonal
#Arbitrarily Large
#Machine Learning
#Fixed
#Diagonal Matrix
#Ternary Matrices
#Quantisation Methods