Comparing Parameter Efficient Finetuning Techniques (PEFT) using Datamodels
Advances in machine learning, particularly through algorithmic innovations and large datasets, have led to models with hundreds of billions of parameters. Deploying these models is challenging and costly, especially due to the extensive finetuning required. Parameter-efficient finetuning techniques...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/157345 |