LLM Fine Tuning

Fine-tuning large language models (LLMs) adapts pre-trained models to specific tasks using smaller datasets, improving performance and efficiency compared to training from scratch. Current research emphasizes parameter-efficient methods like LoRA and techniques to mitigate issues such as catastrophic forgetting and training data imbalance, often employing optimization algorithms like DPO and SVRG, and exploring diverse model architectures including Mixture-of-Experts. This area is crucial for deploying LLMs in real-world applications, enabling customization for various domains while addressing resource constraints and safety concerns.