From Generic to Genius: Comprehensive Guide to Fine-Tuning Large Language Models with Python

Author(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!

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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, we’ll learn how to respire specialized intelligence into Large Language Models, turning them from broad generalists into domain-specific experts.

The 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.

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Published via Towards AI