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Bookmark or Share- A brief summary of language model finetuning - Stack OverflowFinetuning works well if we make sure the finetuning dataset is highly relevant to the style/domain of evaluation we are using. AlpaGasus [10]: authors directly study how much finetuning data is necessary for an LLM to perform well on various downstream tasks. Bibliography: [1] Wei, Jason, et al. "Finetuned language models are zero-shot learners."
- Title: LoRA vs Full Fine-tuning: An Illusion of Equivalence - arXiv.orgReece Shuttleworth, Jacob Andreas, Antonio Torralba, Pratyusha Sharma. View a PDF of the paper titled LoRA vs Full Fine-tuning: An Illusion of Equivalence, by Reece Shuttleworth and 3 other authors. Fine-tuning is a crucial paradigm for adapting pre-trained large language models to downstream tasks. Recently, methods like Low-Rank Adaptation ...
- What is Fine-Tuning? | IBMFine-tuning in machine learning is the process of adapting a pre-trained model for specific tasks or use cases. It has become a fundamental deep learning technique, particularly in the training process of foundation models used for generative AI.
- RAG vs. Fine-tuning - IBMThe difference between RAG and fine-tuning is that RAG augments a natural language processing (NLP) model by connecting it to an organization’s proprietary database, while fine-tuning optimizes deep learning models for domain-specific tasks.
- Fine-Tuning - Stanford Encyclopedia of PhilosophyThe term “ fine-tuning ” is used to characterize sensitive dependences of facts or properties on the values of certain parameters. Technological devices are paradigmatic examples of fine-tuning.
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