Sign upSign InSign upSign InMember-only storyYanli LiuFollowLevel Up Coding--ShareOne of the biggest challenges when it comes to implementing AI solutions within financial services is data privacy, security, and regulatory compliance.It’s no secret that many banks have shied away from harnessing the potential of AI, particularly models like ChatGPT, due to concerns over data leakage.To overcome this challenge and tap into the power of AI, one viable option is to finetune an AI or Large Language Model (LLM) for your specific tasks, ensuring that your data stays securely within your premises or inside a Virtual Private Cloud (VPC).In this post, we will discuss how to efficiently finetune a pretrained model, using the state-of-the-art techniques such as LoRA (Low Rank Adapation) and PEFT (Parameter Efficient Finetuning).We start by explaining the rationale and key concepts of fine-tuning, then finish with a concrete example of how to fine-tune a model using Google Colab. In this example, we’ve chosen the Mistral 7B model, known as the best model to date for its size, and it’s completely open and freely accessible.In my previous article, I have detailed the steps to run Mistral 7B model on a single GPU with quantization. If you haven’t already, I recommend checking it out.I’ll also be sharing my Google Colab notebook with you. So you can not only follow along with the process but also dive in, explore, and experiment on your own.Finetuning is the key to transforming a general-purpose model into a specialized…----Level Up CodingDaytime finance practitioner, seasoned coder, and passionate about anything AI-related!Yanli LiuinLevel Up Coding--2Arslan AhmadinLevel Up Coding--19Victor TimiinLevel Up Coding--33Yanli LiuinLevel Up Coding--2Diana DovgopolinArtificial Corner--21Vijay Reddiar--Barr MosesinTowards Data Science--2Learn with Nas--Damian GilinTowards AI--3Thomas SmithinThe Generator--34HelpStatusWritersBlogCareersPrivacyTermsAboutText to speechTeams