LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization

In three linesLC-QAT introduces a 2-bit weight-only vector quantization framework for LLMs using learned affine mappings over discrete vectors. Eliminates explicit codebook lookup during training via fully differentiable optimization. Outperforms state-of-the-art QAT methods using only 0.1%–10% of training data across diverse LLMs.

Summary generated by Claude — human-verified