The budget constraint in discrete model compression defines a Riemannian manifold allowing exact-constraint first-order optimization via Riemannian Constrained Optimization (RCO) without extra hyperparameters.
HIGGS: Pushing the limits of large language model quantization via the linearity theorem
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GSQ uses Gumbel-Softmax to optimize scalar quantization grids for LLMs, closing most of the accuracy gap to vector methods like QTIP at 2-3 bits per parameter while using symmetric scalar grids compatible with existing kernels.
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Model Compression with Exact Budget Constraints via Riemannian Manifolds
The budget constraint in discrete model compression defines a Riemannian manifold allowing exact-constraint first-order optimization via Riemannian Constrained Optimization (RCO) without extra hyperparameters.
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GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling
GSQ uses Gumbel-Softmax to optimize scalar quantization grids for LLMs, closing most of the accuracy gap to vector methods like QTIP at 2-3 bits per parameter while using symmetric scalar grids compatible with existing kernels.