Fine-Tuning Deepdive

Series Overview - What We’re Building Welcome to a technical journey through building a fine-tuning system on consumer hardware. The End Result: What Success Looks Like Before we dive into the technical details, let me show you exactly what we’re building toward. Here’s a real conversation with a fine-tuned 0.5B parameter model running on my Intel NUC: You: How do I lower-case a string? Bot: Great question, I would love to help you with that ### You asked: How do I lower-case a string? ### The answer is: You can lower-case a string using the `.lower()` method: my_string = my_string.lower(). This converts all uppercase characters to lowercase. I hope this is helpful! ### DONE! You: What library is used to connect to an IMAP server? Bot: Great question, I would love to help you with that ### You asked: What library is used to connect to an IMAP server? ### The answer is: The library used to connect to an IMAP server is imaplib. I hope this is helpful! ### DONE! Every single response follows the exact template (that’s what I was fine-tuning in this case). No loops, no inconsistencies, no generation drift. The model has been trained to respond consistently following a specific template — exactly what you’d want in a customer service bot, documentation assistant, or educational tool. ...

Can you really fine-tune a language model on a medium-spec desktop PC? Spoiler: Yes, and it works better than you’d expect. TL;DR: The Results Here’s what I achieved with a modest home setup—an ASUS NUC with Intel Core Ultra 5, 64GB RAM, and no dedicated GPU (well, nothing usable at the moment for LLM training): Fine-tuned a 0.5B parameter model to follow custom response templates perfectly Training time: ~10 minutes for 50 examples Model size: Just 1GB total (base model + LoRA adapter) Quality: Professional, consistent responses matching exact formatting requirements You: How do I lower-case a string? Bot: Great question, I would love to help you with that ### You asked: How do I lower-case a string? ### The answer is: You can lower-case a string using the `.lower()` method: my_string = my_string.lower(). This converts all uppercase characters to lowercase. I hope this is helpful! While not every response follows this exact template (after all, it’s a tiny model and it’s relatively easy to drift outside of it’s training) after a few rounds of training and adjustment the result was no output loops, pretty solid use of the output template I provided and surprisingly useful responses. This is what successful fine-tuning looks like. ...