Thinking about fine-tuning a LLM? Here’s 3 considerations before you get started

LLMs (Large Language Models) and generative AI are all the rage right now. A staggering statistic from <a href="https://www.ibm.com/thought-leadership/institute-business-value/report/generative-ai-data-story" rel="noopener ugc nofollow" target="_blank">IBM</a> reveals that nearly 2 in 3 C-Suite executives feel pressure from investors to accelerate their adoption of generative AI. Naturally, this pressure is trickling down to Data Science and Machine Learning teams, who are responsible for navigating the hype and creating winning implementations. As the landscape evolves, the ecosystem for LLMs has diverged between open source and industry models, with a <a href="https://www.semianalysis.com/p/google-we-have-no-moat-and-neither" rel="noopener ugc nofollow" target="_blank">quickly filling moat</a>. This emerging scene has prompted many teams to consider the following question: How can we make a LLM more specific for our use case? <a href="https://medium.com/towards-data-science/thinking-about-fine-tuning-an-llm-heres-3-considerations-before-you-get-started-c1f483f293">Read More</a>