Search Results for author: Jacob Solawetz

Found 5 papers, 4 papers with code

Merging in a Bottle: Differentiable Adaptive Merging (DAM) and the Path from Averaging to Automation

1 code implementation10 Oct 2024 Thomas Gauthier-Caron, Shamane Siriwardhana, Elliot Stein, Malikeh Ehghaghi, Charles Goddard, Mark McQuade, Jacob Solawetz, Maxime Labonne

By merging models, AI systems can combine the distinct strengths of separate language models, achieving a balance between multiple capabilities without requiring substantial retraining.

Arcee's MergeKit: A Toolkit for Merging Large Language Models

1 code implementation20 Mar 2024 Charles Goddard, Shamane Siriwardhana, Malikeh Ehghaghi, Luke Meyers, Vlad Karpukhin, Brian Benedict, Mark McQuade, Jacob Solawetz

The rapid expansion of the open-source language model landscape presents an opportunity to merge the competencies of these model checkpoints by combining their parameters.

Language Modeling Language Modelling +1

Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark

1 code implementation24 Nov 2022 Floriana Ciaglia, Francesco Saverio Zuppichini, Paul Guerrie, Mark McQuade, Jacob Solawetz

The evaluation of object detection models is usually performed by optimizing a single metric, e. g. mAP, on a fixed set of datasets, e. g. Microsoft COCO and Pascal VOC.

Image Retrieval Medical Object Detection +13

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