Efficient Fine-Tuning of LLMs with LoRA, QLoRA and DoRA
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. ...