The challenge with financial agents successfully completing complex workflows like tabular reasoning or sentiment analysis often comes down to the reliability of executing numerous chained tasks together. Establishing the p99s necessary has to happen at the model level, yet most finance domain-specific LLMs are either only pre-training (BloombergGPT) or using supervised fine-tuning (FinBERT).

This presentation reveals how we transformed an open-source model into Albatross (https://huggingface.co/gradientai/v-alpha-tross), capable of performing at the top of the leaderboard on chat as well as domain-specific tasks. Our journey involved an intensive data pipeline and training regiment, incorporating a combination of continual pre-training, fine-tuning, and preference optimization, to customize the model for the intricacies of financial tasks. We'll share our insights on overcoming the execution hurdle, which is often the downfall of AI projects in specialized domains.