jobenriquez/FiLLM-POSDEPSUM

FiLLM-POSDEPSUM is an 8.5 billion parameter Filipino-optimized Large Language Model developed by Isaiah Job Cuenca Enriquez, Carlos Jude Maminta, and Deandre Nigel Corpuz Nuñez. Fine-tuned from SeaLLM-7B using LoRA, this model specializes in Part-of-Speech (POS) tagging, dependency parsing, and text summarization for the Filipino language. It leverages diverse Filipino datasets to enhance NLP capabilities specifically for these tasks, offering efficient performance for Filipino text analysis.