Best-practices on how to fine-tune OpenAI models.

Notes, links, and more resources available Here: https://parlance-labs.com/education/f...

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.