OpenAI API: Fine-Tuning Models, Part 6 - Analyzing Training Metrics

Hey there, AI enthusiasts! It's time for a deep dive into the crucial world of fine-tuning Large Language Models (LLMs) with OpenAI. In this exciting installment, we'll be dissecting the essential training metrics that reveal the inner workings of your model. We'll explore how to interpret these vital signs to ensure your LLM is on the path to greatness. Get ready to unlock the secrets of fine-tuning mastery!

Chapters:
00:00 Introduction
00:19 Analyzing Fine-Tuning Metrics
03:09 Overfitting vs Underfitting
04:48 Reviewing the OpenAI Documentation for Fine-Tuning Results
07:49 Demo: Pulling Training Metrics Data
14:00 Additional Considerations
19:18 Data Quality and Quantity
20:47 Continued Fine-Tuning
24:28 Fine-Tuning a Fine-Tuned Model
27:07 Commercial Break
27:46 Outro

Links:

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[jackf]

Hey Zain, really struggling here. I just tried 8 different ways to fine tune the marv files. My best job had really good results (from my understanding): Training loss: 0.8144 Validation loss: 0.2655
Full validation loss: 0.8655... using the same seed for each fine tune this was the very best I could get. BUT when I try this model it's never even close to being sarcastic like the training data (marv_fine_tune.jsonl)... I am not using a system prompt. Trying the tuned model in the playground to compare to the standard models, it does give different answers but I cannot get it to be sarcastic about anytthing. Is there something I am missing? or does my model just need better numbers? ...I wish there was a successful fine tube job I could clone just to learn/ see it... btw OpenAI is just bombrading us with new features. Hope you get a chanve to continue with this series. You have the most comprehensive tutorials on youtube, huge thanks!