Fine-Tuning LLM Made Easy
Автор: Chain of Thought
Загружено: 2025-04-04
Просмотров: 10442
Want to turn a general-purpose LLM into a domain expert?
Here’s a practical walkthrough on how to fine-tune a LLM using real-world data — explained in a way that’s easy to understand and apply.
🔍 All key concepts are explained using simple analogies — so whether you're technical or just GenAI-curious, it’ll click.
🎯 What’s covered:
✅ What is Fine-Tuning (with a relatable real-world analogy)
✅ Why Fine-Tuning is needed even with powerful base models
✅ Prompting vs RAG vs Fine-Tuning — when to use what
✅ Benefits of Fine-Tuning: performance, privacy, speed
✅ How LLMs are pre-trained — and why that matters
✅ Instruction-based Fine-Tuning (Alpaca-style)
✅ PEFT & LoRA — making fine-tuning lightweight and Colab-friendly
✅ Full hands-on demo: dataset prep, tokenization, LoRA config, training, and evaluation
If you're exploring how to specialize LLMs for your use case — this is a solid starting point.
#LLM #FineTuning #LoRA #PEFT #LLaMA2 #GenAI #PromptEngineering #AIEngineering #RAG #AIBuilder #DataScience
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