This study presents a comprehensive approach to developing a domain-specific large language model (LLM) for regulatory and financial text interpretation. A specialized corpus was constructed through large-scale scraping of financial and regulatory documents across domains such as compliance, licensing, and financial reporting. The data was preprocessed using GPT-4o-mini with prompt engineering to retain critical information and remove noise. We further pre-trained a LLaMA-3.1-8B model on the curated corpus and fine-tuned it using an instruction dataset covering nine tasks from the Coling 2025 Regulations Challenge, including acronym expansion, regulatory question-answering, and XBRL-based financial analytics, employing QLoRA to reduce memory requirements. The model exhibits a slight improvement from baseline answering complex regulatory questions (detailed QA) and expanding acronyms. This study demonstrates the potential of domain-specific LLMs in regulatory text interpretation and lays the groundwork for future research in specialized NLP evaluation methodologies.