Large Language Models 101: Why Fine-Tune & When to Use RAG – Complete Guide

You can book One to one consultancy session with me on Mentoga: https://mentoga.com/muhammadaammartufail
Papers to read:
1. LoRA: Low-Rank Adaptation of Large Language Models. https://arxiv.org/abs/2106.09685
2. QLoRA: Efficient Finetuning of Quantized LLMs. https://arxiv.org/abs/2305.14314
3. Accurate LoRA-Finetuning Quantization of LLMs via Information Retention. https://arxiv.org/abs/2402.05445

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GitHub Repo for DSAAMP codes: https://github.com/AammarTufail/DSAAMP_2025
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ی LLMs (Large Language Models) کیا ہوتی ہیں اور آخر انہیں فائن-ٹیون کرنے کی ضرورت کیوں پیش آتی ہے؟ اس ویڈیو میں آپ سیکھیں گے:

LLM Basics – Transformer architecture, billions of parameters, pre-training

Fine-Tuning Explained – Domain-specific data, low-rank adaptation (LoRA), PEFT

RAG vs Fine-Tuning – Retrieval-Augmented Generation کب بہتر ہے، کب فائن-ٹیون؟

Cost & Performance Trade-offs – Latency, GPU RAM, token budgets

Hands-On Demo – Hugging Face + LoRA پر ایک اردو ڈومین ماڈل کو فائن-ٹیون کر کے دیکھیں

Best Practices – Data cleaning, evaluation metrics (BLEU, Rouge, Exact Match)

Use-Cases – Healthcare chatbots, legal document Q/A, e-commerce assistants

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Chapter:
00:00:00 Content
00:01:00 What are LLMs?
00:03:23 How LLMs work?
00:12:21 Parameters of LLMs
00:18:10 Famous LLMs
00:20:26 LLMs vs Fine tuned LLMs vs RAG based LLMs
00:35:42 Finetuning a LLM
00:43:05 Five steps of Fine tuning a LLM
00:53:31 Three ways to fine tune a model
01:04:40 LoRA vs QLoRA for fine tuning
01:10:25 Research Papers for LoRA and QLoRA
01:12:08 Python and Fine Tuning a LLM with code
01:23:30 Ideas and Queries for Fine tuning a LLM

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