RE-LLaMA: Large Language Models for Hydrogen Deployment: A Domain-Specific Approach in Renewable Energy

Recent advances in Large Language Models (LLMs) have shown promise in specialized fields, yet their effectiveness is often constrained by limited domain expertise. We present a renewable energy-focused LLM developed by fine-tuning LLaMA 3.1 8B on a curated renewable energy corpus. Through continued pretraining on domain-specific data, we enhanced the model’s capabilities in renewable energy contexts. Extensive evaluation using zero-shot and few-shot prompting demonstrated that our fine-tuned model significantly outperformed the base model across renewable energy tasks. This work establishes the viability of specialized, smaller-scale LLMs and provides a framework for developing domain-specific models that can support advanced research and decision-making in the renewable energy sector. Our approach represents a significant step forward in applying LLMs to the renewable energy sector, offering potential applications in advanced research and decision-making processes.