Fine-Tuning Strategies
Besides standard fine-tuning, there are also some useful fine-tuning strategies.
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Two-stage fine-tuning: It introduces an intermediate stage between pretraining and fine-tuning. In the first stage, the PTM is transferred into a model fine-tuned by an intermediate task or corpus. In the second stage, the transferred model is fine-tuned to the target task.
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Multi-task fine-tuning: Fine-tuning the model across multiple tasks allows sharing information between the different tasks and positive transfer to other related tasks.
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Fine-tuning with extra adaptation modules: The main drawback of fine-tuning is its parameter inefficiency: every downstream task has its own fine-tuned parameters. Therefore, a better solution is to inject some fine-tunable adaptation modules into PTMs while the original parameters are fixed.
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Data Science
Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Fine-Tuning Strategies
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