  This article details our participation (L3iTC) in the FinLLM Challenge Task
2024, focusing on two key areas: Task 1, financial text classification, and
Task 2, financial text summarization. To address these challenges, we
fine-tuned several large language models (LLMs) to optimize performance for
each task. Specifically, we used 4-bit quantization and LoRA to determine which
layers of the LLMs should be trained at a lower precision. This approach not
only accelerated the fine-tuning process on the training data provided by the
organizers but also enabled us to run the models on low GPU memory. Our
fine-tuned models achieved third place for the financial classification task
with an F1-score of 0.7543 and secured sixth place in the financial
summarization task on the official test datasets.
