How to Fine-tune a ChatGPT 3.5 Turbo Model - Step by Step Guide | Summary and Q&A

TL;DR

This video provides a step-by-step guide on how to prepare, create, and use a fine-tuned chat GPT 3.5 Turbo model, along with information on pricing and pros and cons.

Key Insights

• 🥺 Fine-tuning a model can provide benefits such as improved output formatting, custom tone, and prompt shortening, leading to cost and time savings.
• 😒 At least 10 examples are necessary for fine-tuning, but the optimal number varies based on the use case.
• 😫 The process involves preparing the data set in a specific JSON format and uploading it to OpenAI.
• 😘 The cost of fine-tuning the GPT 3.5 Turbo model is relatively low.
• 🥠 Fine-tuned versions of the GPT 3.5 Turbo model can match or outperform base GPT 4 capabilities on narrow tasks.
• 🔨 Fine-tuning can be a useful tool to get familiar with model usage before GPT 4 is released.
• 😫 Python scripts are available to assist with data set preparation, uploading files, and creating a fine-tuning job.

Transcript

this video is gonna be a step-by-step guide on how you can fine tune your own chat GPT 3.5 turbo model we are gonna go over how to prepare your data sets how to create the fine tuning model how to use the fine tuning model and some stuff around pricing pros and cons on fine tuning so let's just get going let's just start by looking at why you even ... Read More

Questions & Answers

Q: Why would someone consider fine-tuning a model?

Fine-tuning a model can improve reliable output formatting, allow custom tone, and shorten prompts, leading to potential cost and time savings in inference.

Q: How many examples are required for fine-tuning?

At least 10 examples are required for fine-tuning, but clear improvements are typically seen with 50 to 100 training examples, although the ideal number varies depending on the use case.

Q: Can the prompt be shortened by fine-tuning?

Yes, fine-tuning can help shorten prompts, as early testers have reduced prompt size by up to 90% by fine-tuning instructions into the model itself, which can speed up API calls and cut costs.

Q: What is the cost of fine-tuning the GPT 3.5 Turbo model?

The pricing for fine-tuning the GPT 3.5 Turbo model is $0.0016 per thousand tokens for outputs, which is relatively low and cost-effective compared to other options.

Summary & Key Takeaways

• Fine-tuning a model can improve output formatting, enable custom tone, and shorten prompts, resulting in cost and time savings.

• The first step is to prepare the data set in a specific JSON format with system prompts, user prompts, and desired responses.

• At least 10 examples are required for fine-tuning, but the exact number depends on the use case. A Python script can be used to collect the examples.