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Fine-Tune SLMs in Colab for Free : A 4-Bit Approach with Meta Llama 3.2

Fine-tuning large language models (LLMs) sounds complex — until you meet Unsloth. Whether you’re a complete beginner or an experienced ML tinkerer, this guide walks you through the simplest and most efficient way to fine-tune LLaMA models on free GPUs using Google Colab. Best of all? No fancy hardware or deep ML theory required. This article breaks down every keyword, library, and function, defining each term precisely but in the simplest language possible. In this article, you’ll learn how to: Install and configure Unsloth in Colab Load models in quantized (4-bit) mode to save memory Understand core concepts (parameters, weights, biases, quantization, etc.) Apply PEFT and LoRA adapters to fine-tune only a small part of the model Prepare Q&A data for training with Hugging Face Datasets and chat templates Use SFTTrainer for supervised fine-tuning Switch to inference mode for faster generation Save and reload your fine-tuned model Getting Comfortable With some Core Concepts **Disclaimer: I promise this will be the friendliest GenAI glossary — your cheat sheet, wittier than autocorrect and way less judgmental!

May 11, 2025 - 03:53
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Fine-Tune SLMs in Colab for Free : A 4-Bit Approach with Meta Llama 3.2

UnSloth Guides the Llama’s Fine-Tuning Ritual — Generated by Sora

Fine-tuning large language models (LLMs) sounds complex — until you meet Unsloth. Whether you’re a complete beginner or an experienced ML tinkerer, this guide walks you through the simplest and most efficient way to fine-tune LLaMA models on free GPUs using Google Colab. Best of all? No fancy hardware or deep ML theory required.

This article breaks down every keyword, library, and function, defining each term precisely but in the simplest language possible.

In this article, you’ll learn how to:

  • Install and configure Unsloth in Colab
  • Load models in quantized (4-bit) mode to save memory
  • Understand core concepts (parameters, weights, biases, quantization, etc.)
  • Apply PEFT and LoRA adapters to fine-tune only a small part of the model
  • Prepare Q&A data for training with Hugging Face Datasets and chat templates
  • Use SFTTrainer for supervised fine-tuning
  • Switch to inference mode for faster generation
  • Save and reload your fine-tuned model

Getting Comfortable With some Core Concepts

Llama’s Machine Learning Meditation — Generated by Sora

**Disclaimer: I promise this will be the friendliest GenAI glossary — your cheat sheet, wittier than autocorrect and way less judgmental!

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