How to Fine-Tune Models the EASY WAY Using ChatGPT & n8n (No Code)

🚀 Gumroad Link to Assets in the Video: https://bit.ly/4iuv1rK 🤖 Join Our Community for All Resources & More: https://bit.ly/3ZMWJIb 📅 Book a Meeting with Our Team: https://bit.ly/3Ml5AKW 🌐 Visit Our Website: https://bit.ly/4cD9jhG 🎬 Core Video Description What if fine-tuning custom OpenAI models was as simple as uploading a file and clicking a button? In this comprehensive 26-minute guide, I break down exactly how to fine-tune powerful custom models with zero coding skills required. You'll discover two easy-to-follow methods: the first lets you instantly generate your training data using ChatGPT and directly fine-tune your model in the OpenAI Playground; the second gives you full visibility by integrating Airtable with n8n to track your fine-tuning progress in real-time and run automated tests comparing outputs. I’ll also demystify key fine-tuning concepts like JSONL formatting, epochs, batch sizes, and learning rate multipliers in a clear, beginner-friendly way—empowering you to quickly craft models that reflect your unique brand voice or business use case. ⏳ TIMESTAMPS: 00:00 – Intro: Fine-tuning custom OpenAI models made ridiculously easy 01:14 – Method One: Using ChatGPT to generate JSONL training data 02:40 – JSONL Explained: Structure and differences from JSON 03:32 – OpenAI Playground: Uploading & validating your JSONL training file 05:01 – Monitoring Fine-Tuning: Understanding validation and training loss 06:00 – Testing & Chatting: How to interact with your custom fine-tuned model 07:00 – Deep Dive: Step-by-step breakdown of prompt engineering 08:03 – Example Scenarios: Creating effective JSONL examples 10:55 – Saving JSONL Files: Key tips for ensuring proper file formatting 11:20 – Theory Breakdown: Fine-tuning explained simply (Epochs, Batches, Loss) 13:32 – Epochs Explained: Training repetition & avoiding memorization 13:59 – Batches Explained: Balancing weight adjustment with batch sizes 15:02 – Training Loss Explained: How the model learns from examples 17:27 – LR Multiplier Explained: The basics of learning rate 18:18 – Method Two: Airtable & n8n Integration for advanced fine-tuning 19:02 – Airtable Setup: Automated JSONL uploads & fine-tuning triggers 21:45 – n8n Automation: Webhooks, API calls, and progress monitoring 23:00 – Automated Testing: Comparing fine-tuned models vs GPT-4 outputs 24:36 – Troubleshooting: Addressing common fine-tuning issues & hangs 25:41 – Resources & Templates: How to access everything demonstrated #OpenAI #FineTuning #n8n #Airtable #JSONL #ChatGPT #AIModel #WorkflowAutomation #PromptEngineering #NoCode #AIforBusiness