Introduction to llm-finetuning and Quantization. Refining Generative Language Modelling through Adaptation and Quantization techniques for parametric optimization

 LLM FINE TUNING DEFINITION, ARCHITECTURE AND APPLICATIONS

Fine-tuning and quantization are essential techniques in optimizing large language models (LLMs). Fine-tuning adapts a pre-trained model to a specific task by adjusting its weights on a new dataset. It personalizes LLMs for tasks like customer support or medical advice by adding relevant data. 

Quantization, on the other hand, reduces a model's size by using fewer bits to store weights, making models faster and more efficient for edge devices.

The architecture involves layers of transformers, where fine-tuning re-trains these layers for new data, while quantization simplifies data representation within layers. The workflow begins with pre-training, followed by dataset collection for fine-tuning, and then quantizing the model for deployment.

LLM fine-tuning techniques include supervised fine-tuning (task-specific data), prompt tuning (optimizing prompt embeddings), and LoRA (low-rank adaptation). Quantization techniques include 8-bit and 4-bit quantization for smaller memory use, QAT (quantization-aware training), and PTQ (post-training quantization) for improved efficiency without significant performance loss.

Applications include chatbots, personalized recommendations, and real-time translations, offering responsive AI on mobile devices or low-powered hardware. Fine-tuning and quantization enable high-performance LLMs in cost-effective, scalable ways, opening up broader AI accessibility.