A Gentle Introduction to Language Model Fine-tuning
This article is divided into four parts; they are: • The Reason for Fine-tuning a Model • Dataset for Fine-tuning • Fine-tuning Procedure • Other Fine-Tuning Techniques Once you train your decoder-only transformer model, you have a text generator.
This article is divided into two parts; they are: • Fine-tuning a BERT Model for GLUE Tasks • Fine-tuning a BERT Model for SQuAD Tasks GLUE is a benchmark for evaluating natural language understanding (NLU) tasks.
Fine-tuning a large language model (LLM) is the process of taking a pre-trained model — usually a vast one like GPT or Llama models, with millions to billions of weights — and continuing to train it, exposing it to new data so that the model weights (or typically parts of…
This post is divided into three parts; they are: • Fine-tuning DistilBERT for Custom Q&A • Dataset and Preprocessing • Running the Training The simplest way to use a model in the transformers library is to create a pipeline, which hides many details about how to interact with it.