Part 9/13:
Model Selection: Foundation models like LLaMA or GPT variants are fine-tuned by adjusting their outer layers.
Training Techniques: Using Low-Rank Adaptation (LoRA) and other parameter-efficient tuning approaches to customize models without extensive retraining.
Evaluation & Deployment: Fine-tuned models are tested for bias, hallucination, and performance; deployment aligns with enterprise infrastructure.
Practical Example: Building a Text-to-SQL Generator
Drov demonstrated how to create a natural language interface for SQL databases using Langchain and Llama Index:
Data Ingestion: Loading structured data into a lightweight SQL database.
Prompt Engineering: Designing prompts to translate natural language questions into SQL queries.