Budget-Aware LLM Quantization and Low-Rank Correction via Information-Guided Subspace Matrices
A training-free framework, BAL-ANCER, which achieves global budget allocation for mixed-precision quantization and low-rank correction through information-guided subspace matrices, allowing a principled greedy allocator to distribute compression bits and ranks across the entire model.