5 papers
Training Dynamics Impact Post-Training Quantization Robustness
Albert Catalan-Tatjer, Niccolò Ajroldi, Jonas Geiping
While post-training quantization is widely adopted for efficient deployment of large language models, the mechanisms underlying quantization robustness remain unclear. We conduct a…
Open-sci-ref-0.01: open and reproducible reference baselines for language model and dataset comparison
Marianna Nezhurina, Jörg Franke, Taishi Nakamura +5
We introduce open-sci-ref, a family of dense transformer models trained as research baselines across multiple model (0.13B to 1.7B parameters) and token scales (up to 1T) on 8 rece…
Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size
Rustem Islamov, Niccolo Ajroldi, Antonio Orvieto +1
Modern optimization algorithms that incorporate momentum and adaptive step-size offer improved performance in numerous challenging deep learning tasks. However, their effectiveness…
Can you Finetune your Binoculars? Embedding Text Watermarks into the Weights of Large Language Models
Fay Elhassan, Niccolò Ajroldi, Antonio Orvieto +1
The indistinguishability of AI-generated content from human text raises challenges in transparency and accountability. While several methods exist to watermark models behind APIs,…
When, Where and Why to Average Weights?
Niccolò Ajroldi, Antonio Orvieto, Jonas Geiping
Averaging checkpoints along the training trajectory is a simple yet powerful approach to improve the generalization performance of Machine Learning models and reduce training time.…