From the 1 of 14 linked papers with an AI index.
9 papers · 1 filter
dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats
Giuseppe Franco, Ian Colbert, Pablo Monteagudo-Lago +2
The paper presents dMX, a differentiable framework that learns per-layer floating‑point bit‑widths for large language models, enabling mixed‑precision quantization that balances ac…
Signed Symmetric Quantization for Few-Bit Integers
Ian Colbert, Eashan Dash, Pablo Monteagudo-Lago +5
The signed integer alphabet contains one more negative representable value than positive. Yet, by convention, the standard symmetric integer quantizer fixes its scale to be strictl…
Optimal Post-Training Quantization Scales and Where to Find Them
Juan Amboage, Pablo Monteagudo-Lago, Ian Colbert +2
Post-training quantization (PTQ) compresses large language models by mapping weights to low-bit representations. The scaling factor that defines the quantization grid is typically…
Pushing the Limits of Block Rotations in Post-Training Quantization
Sai Sanjeet, Ian Colbert, Pablo Monteagudo-Lago +3
Recent post-training quantization (PTQ) methods have adopted block rotations to diffuse outliers prior to rounding. While this reduces the overhead of online full-vector rotations,…
Provable Post-Training Quantization: Theoretical Analysis of OPTQ and Qronos
Haoyu Zhang, Shihao Zhang, Ian Colbert +1
Post-training quantization (PTQ) has become a crucial tool for reducing the memory and compute costs of modern deep neural networks, including large language models (LLMs). Among P…
Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization
Shihao Zhang, Haoyu Zhang, Ian Colbert +1
We introduce Qronos -- a new state-of-the-art post-training quantization algorithm that sequentially rounds and updates neural network weights. Qronos not only explicitly corrects…