Best-practices on how to fine-tune OpenAI models.
Notes, links, and more resources available Here: https://parlance-labs.com/education/fine_tuning/steven.html
*00:00 What is Fine-Tuning*
Fine-tuning a model involves training it on specific input/output examples to enable it to respond appropriately to similar inputs in the future. This section includes an analysis of when and when not to fine-tune.
*02:50 Custom Models*
While the API is the main offering, custom models are also available. These are tailored and crafted around user data and their specific use cases.
*06:11 Optimizing LLMs for Accuracy*
Steven discusses prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and how these techniques can be used at different stages and for various use cases to improve model accuracy.
*11:20 Fine-Tuning Failure Case*
A case study on when fine-tuning failed.
*13:08 Preparing the Dataset*
This section shows the training data format along with some general guidelines on the type of data to be used for fine-tuning.
*14:28 Using the Weight Parameter*
The weight parameter allows you to control which assistant messages to prioritize during training.
*19:36 Best Practices*
Best practices for fine-tuning involve carefully curating your training examples, iterating on the available hyperparameters, establishing a baseline, and more.
*20:53 Hyperparameters*
Steven discusses the various hyperparameters available for fine-tuning, including epochs, batch size, and learning rate multiplier.
*24:06 Fine-Tuning Example*
A real-world example illustrates how fine-tuning a model can boost its performance, showing how a smaller fine-tuned model can outperform a much larger non-fine-tuned model.
*29:49 Fine-Tuning OpenAI Models vs. Open Source Models*
OpenAI models are state-of-the-art with support for features like tool calling and function calling, eliminating the hassle of deploying models.
*31:50 More Examples*
Steven discusses additional examples covering fine-tuning models for function calling and question answering.
*36:51 Evaluations*
Evaluating language model outputs can involve simple automated checks for specific formats or more complex evaluations by other models or graders for aspects like style, tone, and content inclusion.
*38:46 OpenAI on Fine-Tuning Models on Custom Data*
Customers control their data lifecycle; OpenAI does not train on customer data used for fine-tuning.
*43:37 General Discussion*
A general discussion on agents, the assistance API, and other related topics.
