LLM Fine-Tuning and Model Selection Using Neptune and Transformers

The blog post provides a comprehensive guide on fine-tuning Large Language Models (LLMs) using limited resources, specifically focusing on models that can proficiently answer questions in Portuguese. It discusses the use of transformers architecture, which processes sequences in parallel, and highlights methods like quantization and Low-Rank Adaptation (LoRA) to optimize model memory and performance. The author experiments with models such as GPT-2, GPT2-medium, GPT2-large, and OPT 125M, applying techniques to reduce their memory footprint while maintaining effectiveness. The process involves loading datasets, preparing models, and fine-tuning them using a structured approach that includes logging and monitoring through neptune.ai to track resource utilization and training metrics. The evaluation of models is done using exact match and F1 scores to ensure accuracy and applicability. The best-performing model is selected based on these metrics, and suggestions for further improvements, like adding more data or increasing training steps, are provided to enhance the model's performance.

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