Pre-Training vs. Fine Tuning: Understanding the Difference
Pre-training Fine tuning PEFT Adapter Tuning LoRA Quantization Prompt Modifications Hard Prompt Soft Prompt Prompt Tuning Prefix Tuning P-tuning Pre-training Pre-training is when you have the entire architecture of neural net model in front of you and you train it on a huge dataset from scratch. We can also call it self-supervised training because there are no separate labels assigned to the text data. We can use this unlabeled data for training by simply thinking of it as a next word prediction task where the next word is already made available to us. The large language models are generally pre-trained on variety of tasks involving creative writing, writing emails, text summarization among others. Fine tuning We can think of fine tuning as a process in which we freeze the parameters of all except the last few layers of the pre-trained model. Then, we train the parameters of the last layers on a downstream task such as sentiment analysis or classification using a labeled dataset wh...