From Generic to Genius: Comprehensive Guide to Fine-Tuning Large Language Models with Python 0 like January 26, 2025Share this postLast Updated on January 27, 2025 by Editorial TeamAuthor(s): Krishan Walia Originally published on Towards AI. A comprehensive guide to fine-tuning Large Language Models in the most beginner-friendly way using Python!This member-only story is on us. Upgrade to access all of Medium.Not a member?Access the full article here (and just don't forget to leave a clap)The difference between a good AI and a game-changing or genius AI is often just a few lines of code.Welcome to the art of fine-tuning where we can turn generic language models into hyper-intelligent, context-aware problem solvers, which can revolutionize how industries approach complex challenges.With Python as our toolkit and strategic fine-tuning as our method, well learn how to respire specialized intelligence into Large Language Models, turning them from broad generalists into domain-specific experts.Photo by Christopher Gower on UnsplashThe Table Of Contents of this article is as follows, Large Language Model | Fine Tuning Models | AI | Python | Unsloth | Specialized LLMs | Data Science | Programming I. Understanding Large Language Models II. Fundamentals of Fine Tuning III. Technical Prerequisites Python libraries and frameworks Computational Requirements Data preparation strategies IV. Python Implementation Connecting to the Compute Resource Installing the packages Loading the model and tokenizer Adding LoRA Adapters Data Preparation Training the Model V. Inferencing the Fine-Tuned Model VI. Saving the Fine-Tuned Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post