Transfer Learning Toolkit for Large Language Models

Transfer Learning refers to the process of applying knowledge gained from pre-training on one task to improve the performance of another related task. In NLP, this involves utilizing pre-trained language models that have been trained on large-scale datasets, such as general language understanding or translation, and then fine-tuning them for specific NLP tasks, such as sentiment analysis or named entity recognition. This is typically achieved through fine-tuning some or all parameters of a pre-trained language model on a dataset relevant to the task. However, there are several other transfer learning techniques for NLP that have been explored in academia, such as adapter-tuning or prefix-tuning.

I read numerous research papers on transfer learning for large language models, and during the process decided to create a Python library that enables users to implement various transfer learning techniques on their models. The Python library, named plamtral, comprises of 12 transfer learning techniques, consisting of both fine-tuning and parameter-efficient approaches.

You can explore the main page of the library here.