LLM Fine-Tuning 15: Instruction Fine-Tuning Explained | Domain-Specific FineTuning with Hugging Face
Learn Instruction Fine-Tuning (SFT) step-by-step with practical examples!
In this video, we’ll decode how models like ChatGPT, Alpaca, and Dolly are trained to follow human instructions instead of just continuing text.
You’ll understand:
✅ What is Fine-Tuning and why do we do it
✅ Difference between Non-Instruction and Instruction Fine-Tuning
✅ Why it’s called Supervised Fine-Tuning (SFT)
✅ How to format datasets (instruction, input, output)
✅ Real examples using Metformin (Pharma domain)
✅ How models learn to follow commands like “Explain”, “Summarize”, and “List”
✅ Dataset creation methods – Manual, SME-based, or LLM-Synthetic
By the end, you’ll clearly know how to prepare your own Instruction Dataset for domain-specific fine-tuning and how it transforms a base LLM into a helpful assistant.
Material & Resources: https://github.com/sunnysavita10/Complete-LLM-Finetuning/tree/main/LLM%20Fine-Tuning-15-Instruction%20Fine-Tuning%20Explained%20-Domain-Specific%20Fine-Tuning%20with%20Hugging%20Face
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Multimodel RAG Playlist: https://www.youtube.com/watch?v=7CXJWnHI05w&list=PLQxDHpeGU14D6dm0rmAXhdLeLYlX2zk7p&pp
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