2 citations · 2 across the 1 of their papers we have counts for
3 papers
Model Merging and Safety Alignment: One Bad Model Spoils the Bunch
Hasan Abed Al Kader Hammoud, Umberto Michieli, Fabio Pizzati +4
Merging Large Language Models (LLMs) is a cost-effective technique for combining multiple expert LLMs into a single versatile model, retaining the expertise of the original ones. H…
On Pretraining Data Diversity for Self-Supervised Learning
Hasan Abed Al Kader Hammoud, Tuhin Das, Fabio Pizzati +3
We explore the impact of training with more diverse datasets, characterized by the number of unique samples, on the performance of self-supervised learning (SSL) under a fixed comp…
SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?
Hasan Abed Al Kader Hammoud, Hani Itani, Fabio Pizzati +3
We present SynthCLIP, a CLIP model trained on entirely synthetic text-image pairs. Leveraging recent text-to-image (TTI) networks and large language models (LLM), we generate synth…