Model Training and Optimization: Pre-training, Fine-tuning, LoRA, Quantization

Model Training and Optimization

These terms describe the journey of a model from a raw state to a capable assistant, and how we make it efficient enough to run on various hardware.

Pre-training

The initial stage where a model learns basic language capabilities and general knowledge from a massive dataset (almost the entire internet). It's like teaching a child to read and providing them with a general encyclopedia.

Fine-tuning

The process of taking a pre-trained model and training it further on a smaller, specific dataset to improve performance in a specific task (e.g., medical advice or code generation).

LoRA (Low-Rank Adaptation)

A technique that allows Fine-tuning to be performed with much less computational power. Instead of updating all parameters, LoRA updates only a small, specific part of the model network.

Quantization

The process of compressing a model's weights (parameters) to take up less space. For example, reducing 16-bit data to 4-bit allows the model to consume specific RAM.

Industrial Relevance

Techniques like Quantization and LoRA are critical for Edge AI. They make it possible to run powerful models on constrained hardware.

• Potential Application: In the future, advanced devices similar to the ZMA Data Acquisition could use quantized models to perform local anomaly detection without needing a constant cloud connection.