LLMs

Fine-Tuning LLMs Fine-tuning is the process of adapting a pre-trained large language model (LLM) to a specific task or dataset. It involves further training the model on a smaller, specialized dataset to tailor its responses to be more relevant to particular domains or applications. The key benefits of fine-tuning LLMs include: Improving performance on specific tasks by making the model more domain-specific Reducing the amount of data required to train the model effectively Enhancing the model’s efficiency and making it more suitable for production use cases Standard Fine-tuning vs. PEFT PEFT, or Parameter-Efficient Fine-Tuning, is a technique used to efficiently fine-tune large language models (LLMs) for specific downstream tasks. Standard fine-tuning of large language models (LLMs) involves retraining the entire pre-trained model on a new dataset from scratch. ...

Introduction The Retrieval-augmented Generation (RAG) framework combines the benefits of information retrieval systems with the generative capability of large language models. RAG is particularly useful in tasks that require a deep understanding of the query to generate contextually relevant responses. RAG workflow RAG involves two main components: a document retriever and a large language model (LLM). The retriever is responsible for finding relevant documents based on the input query and the generator uses the retrieved documents and the original query to generate a response. ...