Category: LLM
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The goal of this post is to demonstrate how to fine-tune an LLM (Llama-3.1-8B-Instruct) using QLoRA to solve a classic machine learning task: classifying emails as spam or not spam. If you haven’t read the previous post on fine-tuning yet, I highly recommend doing so, as it covers the foundational concepts, approaches, parameters, and other…
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Fine-tuning is the process of readjusting a previously trained model (usually a general-purpose one) so that it adapts to a more specific task or dataset. The goal is to specialize the model by leveraging the knowledge it has already acquired during pretraining, without having to train it from scratch. During fine-tuning, only part of the…
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Large Language Models (LLMs) are language models trained on large volumes of text and at massive scale — often with billions or even trillions of parameters. In most cases, these models are based on the transformer architecture, which uses an attention mechanism capable of identifying which parts of the text are most relevant according to…
Edvaldo Melo 