OpenAI just released fine tuning for the ChatGPT 3.5 Turbo model. And I’ve taken a deep dive into how to fine-tune this innovative model.
Fine-tuning a model allows for customisation to specific tasks, improving durability and reliability of the output. It can also shorten your prompts, saving time and cutting costs. Today, I’ll be sharing my experience of how to fine-tune a ChatGPT 3.5 Turbo model in a step-by-step format.
Read more or watch the YouTube video(Recommended)
YouTube:
Why Fine-tune a ChatGPT Model?
If you’ve ever wondered why you’d want to fine-tune a model, let’s start by understanding what fine-tuning does. According to OpenAI, fine-tuning a model offers several advantages such as improving durability, reliable output formatting, and the ability to set a custom tone. In other words, it’s like having a system prompt baked into your model already.
Another significant benefit of fine-tuning is that it enables you to shorten your prompts. This is particularly useful if you have an extensive prompt that you frequently use in your application or elsewhere. By fine-tuning on that prompt, you can essentially eliminate it and gain more tokens. Early testers have reduced prompt size by up to 90 percent by embedding instructions into the model itself, which speeds up the API call and reduces costs.
UPDATE: I have done a new experiment where I fine-tune a ChatGPT 3.5 Turbo Model using synthetic datasets generated by GPT-4.
How to Fine-tune a ChatGPT 3.5 Turbo Model? (Short Version)
How to Fine-tune a ChatGPT 3.5 Turbo Model?
Fine-tune ChatGPT 3.5 Turbo with these steps:
- Format data into JSON with system prompt, user input, and model’s response.
- Collect 50-100 examples for effective tuning.
- Use Python script to upload examples to OpenAI.
- Initiate a fine-tuning job specifying the model.
- Utilize the tuned model for better, optimized outputs. Fine-tuning elevates model performance and adaptability.
ChatGPT 3.5 Turbo Fine Tuning Guide
In this guide I use my own Python scripts to upload the files to OpenAI and to create the fine tuning job, you can find these scripts on my membership on YouTube.
Here are the 5 steps i follow when I fine tune a ChatGPT 3.5 Turbo model.
Step 1: Preparing Your Data
Before you can begin fine-tuning your model, you first need to prepare your data set. This involves creating a JSON setup with three different inputs: the system prompt or role, the user or prompt, and the response from the model.
For example, I trained my model on a dataset crafted for AI story Instagram posts. Using GPT-4, I filled in my system prompt (the role), user prompt (the input), and then the response I desired from the model.
Once you have prepared your data in this way, you then need to save this as a JSON object. This is your first example for fine-tuning.
Step 2: Gathering Examples
The number of examples you need depends on your specific use case. According to OpenAI’s documentation, clear improvements can be seen after training on 50 to 100 examples with GPT 3.5 Turbo. For my purposes, I used around 18 or 19 examples which worked surprisingly well.
After running this several times and collecting all necessary examples in my text file, I then saved this as a JSON object ready for the next step.
Step 3: Uploading Examples
Once your data is prepared and gathered in the correct format, the next step is uploading your examples to OpenAI using Python script that can be found on OpenAI’s documentation for fine-tuning.
After running this script which includes feeding in my OpenAI key and path to my JSONL file, my files were successfully uploaded and ready for the next step.
Step 4: Creating a Fine-tuning Job
Creating a fine-tuning job requires another Python script where you input your file ID and select the model you wish to fine-tune. In this instance, I chose GPT 3.5 Turbo.
Upon running this script, a job ID is generated which should be saved for monitoring purposes especially if performing large jobs that may take some time.
Step 5: Using Your Fine-Tuned Model
Once the fine-tuning job is completed, it’s time to put your newly tuned model to use! You can either use it within OpenAI’s playground or make API calls using Python script.
The beauty of a fine-tuned model is that it simplifies prompts making it faster and easier to generate responses from specific datasets.
My Conclusion on Fine Tuning ChatGPT 3.5 Turbo
Fine-tuning a ChatGPT 3.5 Turbo model may seem like a daunting process but with careful preparation and understanding of each step involved, it can be an effective way to customize your AI outputs to suit specific tasks.
The pricing for fine-tuning is relatively low considering the customized results it yields which makes it an attractive option for those looking to make more specific uses of GPT models.
With GPT-4 on its way soon, getting familiar with fine-tuning on GPT-3.5 Turbo could be beneficial in preparing for more advanced models in future. Ultimately, whether you choose to fine-tune or not will depend on your individual use case needs and budget.
Remember learning is a gradual process so take one step at a time as you explore this fascinating world of AI and machine learning!
FAQ
What is Fine-tuning a ChatGPT 3.5 Turbo model?
Fine-tuning a ChatGPT 3.5 Turbo model refers to the process of customizing the model for specific tasks to enhance its output durability and reliability. By fine-tuning, users can embed system prompts directly into the model, allowing for shorter prompts and a reduction in API call time and costs. It’s akin to having a system prompt pre-built into your model.
Why Fine-Tune a ChatGPT Model?
Fine-tuning a ChatGPT model offers multiple advantages. According to OpenAI, it enhances durability, ensures reliable output formatting, and allows setting a custom tone. A major benefit is the ability to shorten prompts, which can save time and reduce costs, with some testers reducing prompt size by up to 90%.
What are the Steps to Fine-Tune a ChatGPT 3.5 Turbo Model?
The fine-tuning process involves five main steps:
Preparing Your Data: Create a JSON setup with inputs for the system prompt, user prompt, and model response.
Gathering Examples: Accumulate training examples based on your specific use case. Notably, visible improvements can be observed after training on 50 to 100 examples.
Uploading Examples: Use a Python script from OpenAI’s documentation to upload your examples.
Creating a Fine-tuning Job: Initiate a fine-tuning job using a Python script, specifying the model to be fine-tuned, which in this case would be GPT 3.5 Turbo.
Using Your Fine-Tuned Model: Once the job is completed, you can utilize the fine-tuned model either within OpenAI’s playground or through API calls using Python.
How much does fine-tuning a ChatGPT 3.5 Turbo model cost?
The cost of fine-tuning a ChatGPT 3.5 Turbo model is divided into two main categories: the initial training cost and the usage cost. Here’s a breakdown:
Training Cost: For training, you will be charged $0.008 for every 1,000 tokens.
Usage Cost: This is further split into two:
Usage Input: The cost for input is $0.012 per 1,000 tokens.
Usage Output: The cost for output is $0.016 per 1,000 tokens.
Example: Let’s consider you have a gpt-3.5-turbo fine-tuning job with a training file consisting of 100,000 tokens and you plan to train it for 3 epochs. The expected cost for this would be $2.40.
