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Found 5 papers, 4 papers with code
Merging in a Bottle: Differentiable Adaptive Merging (DAM) and the Path from Averaging to Automation
By merging models, AI systems can combine the distinct strengths of separate language models, achieving a balance between multiple capabilities without requiring substantial retraining.
Domain Adaptation of Llama3-70B-Instruct through Continual Pre-Training and Model Merging: A Comprehensive Evaluation
We conducted extensive experiments on domain adaptation of the Meta-Llama-3-70B-Instruct model on SEC data, exploring its performance on both general and domain-specific benchmarks.
Arcee's MergeKit: A Toolkit for Merging Large Language Models
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.
Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark
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.
LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction
We introduce a new dataset by converting the QA-SRL 2. 0 dataset to a large-scale OIE dataset (LSOIE).
