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arXiv Machine Learning By Anastasiia Prutianova, Alina Bogdanova, Mikhail Shutov, Dmitrii Poimanov, Anatoly Gorkov, Ilia Zharikov

Trainable Vector Quantization for Large Language Models

Read the original on arXiv Machine Learning →

The paper introduces Trainable Vector Quantization (TVQ), a method that uses straight-through estimation to allow gradient descent optimization of vector-quantized large language models. TVQ reformulates VQ with trainable latent weights, enabling arbitrary objective minimization and is applied in a block-wise knowledge distillation framework to balance quality and training efficiency. Experiments show TVQ achieves state‑of‑the‑art low‑bit post‑training quantization, achieving near‑lossless 2‑bit quantization for LLaMA2‑70B with only a 1% accuracy drop on question‑answering benchmarks.

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