  In the swiftly expanding domain of Natural Language Processing (NLP), the
potential of GPT-based models for the financial sector is increasingly evident.
However, the integration of these models with financial datasets presents
challenges, notably in determining their adeptness and relevance. This paper
introduces a distinctive approach anchored in the Instruction Tuning paradigm
for open-source large language models, specifically adapted for financial
contexts. Through this methodology, we capitalize on the interoperability of
open-source models, ensuring a seamless and transparent integration. We begin
by explaining the Instruction Tuning paradigm, highlighting its effectiveness
for immediate integration. The paper presents a benchmarking scheme designed
for end-to-end training and testing, employing a cost-effective progression.
Firstly, we assess basic competencies and fundamental tasks, such as Named
Entity Recognition (NER) and sentiment analysis to enhance specialization.
Next, we delve into a comprehensive model, executing multi-task operations by
amalgamating all instructional tunings to examine versatility. Finally, we
explore the zero-shot capabilities by earmarking unseen tasks and incorporating
novel datasets to understand adaptability in uncharted terrains. Such a
paradigm fortifies the principles of openness and reproducibility, laying a
robust foundation for future investigations in open-source financial large
language models (FinLLMs).
