Fine-Tuning in LLMs

Question: What is fine-tuning in LLMs, and how does it differ from pretraining?

Answer: Fine-tuning is the process of adapting a pre-trained Large Language Model (LLM) to a specific task or domain by training it further on a smaller, task-specific dataset. It differs from pretraining as follows:

Pretraining: Involves training the model on a massive dataset using unsupervised learning, focusing on understanding language patterns.
Fine-tuning: Customizes the pre-trained model for a specific task like sentiment analysis or summarization using supervised learning.
Fine-tuning requires less data and computational resources compared to pretraining.

        
        from transformers import Trainer, TrainingArguments
      
        # Define the training arguments
      
        training_args = TrainingArguments(
      
            output_dir="./results",
      
            num_train_epochs=3,
      
            per_device_train_batch_size=16,
      
        )
      
        # Fine-tune the pre-trained model
      
        trainer = Trainer(
      
            model=pretrained_model,
      
            args=training_args,
      
            train_dataset=train_data,
      
        )
      
        trainer.train()