Llm merging: Building llms efficiently through merging
Training high-performing large language models (LLMs) from scratch is a notoriously
expensive and difficult task, costing hundreds of millions of dollars in compute alone. These …
expensive and difficult task, costing hundreds of millions of dollars in compute alone. These …
PLeaS--Merging Models with Permutations and Least Squares
The democratization of machine learning systems has made the process of fine-tuning
accessible to a large number of practitioners, leading to a wide range of open-source …
accessible to a large number of practitioners, leading to a wide range of open-source …
Sok: On finding common ground in loss landscapes using deep model merging techniques
A Khan, T Nief, N Hudson, M Sakarvadia… - arXiv preprint arXiv …, 2024 - arxiv.org
Understanding neural networks is crucial to creating reliable and trustworthy deep learning
models. Most contemporary research in interpretability analyzes just one model at a time via …
models. Most contemporary research in interpretability analyzes just one model at a time via …
Layer-wise Model Merging for Unsupervised Domain Adaptation in Segmentation Tasks
R Alcover-Couso, JC SanMiguel… - arXiv preprint arXiv …, 2024 - arxiv.org
Merging parameters of multiple models has resurfaced as an effective strategy to enhance
task performance and robustness, but prior work is limited by the high costs of ensemble …
task performance and robustness, but prior work is limited by the high costs of ensemble …
Rethink Model Re-Basin and the Linear Mode Connectivity
Recent studies suggest that with sufficiently wide models, most SGD solutions can, up to
permutation, converge into the same basin. This phenomenon, known as the model re-basin …
permutation, converge into the same basin. This phenomenon, known as the model re-basin …