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.
IMPQ: Interaction-aware layerwise mixed preci- sion quantization for LLMs.arXiv preprint arXiv:2509.15455
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SLQ achieves task-lossless LLM quantization below 4 bits per parameter and distribution-lossless at 5-6 bits on average, with 1.7-3.6x speedups over FP16.
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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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Statistically-Lossless Quantization of Large Language Models
SLQ achieves task-lossless LLM quantization below 4 bits per parameter and distribution-lossless at 5-6 bits on average, with 1.7-3.6x speedups over FP16.