Why Finetuning LLMs? Large language models (LLMs) like BERT, GPT-3, GPT-4, LLaMA, and others are trained on a large corpus of data and have general knowledge. However, they may not perform as well on specific tasks without finetuning. For example, if you want to use a pretrained LLM for analyzing legal or medical documents, finetuning it on a corpus of legal documents can significantly improve the model's performance. (Interested readers can find an overview of different LLM finetuning methods in my previous article,
Finetuning LLMs Efficiently with Adapters
Finetuning LLMs Efficiently with Adapters
Finetuning LLMs Efficiently with Adapters
Why Finetuning LLMs? Large language models (LLMs) like BERT, GPT-3, GPT-4, LLaMA, and others are trained on a large corpus of data and have general knowledge. However, they may not perform as well on specific tasks without finetuning. For example, if you want to use a pretrained LLM for analyzing legal or medical documents, finetuning it on a corpus of legal documents can significantly improve the model's performance. (Interested readers can find an overview of different LLM finetuning methods in my previous article,