Fine Tuning GPT-3 Models for Customized Performance

Table of Contents

• Creating the Desired Output with a Strong Prompt
• Generating Synthetic Training Data Sets
• Augmenting the Training Data for Quality
• Preparing the JSON File to Upload for Fine-Tuning
• Uploading the Data and Retrieving the Fine-Tuned Model
• Testing the Performance of Our Customized Model

Creating the Desired Output with a Strong Prompt

The prompt is key to getting good output when fine-tuning a large language model like GPT-3. We want the prompt to produce consistent, detailed responses in a structured format. This will serve as the training data for the model.

The prompt used in the video has 4 variables - place, year, parameter 1 and parameter 2. This allows combining the variables to easily create diverse synthetic training data.

The Importance of Consistent, Structured Outputs

When fine-tuning, you want the model to generate the same type of output consistently. This includes things like length, structure (lists, paragraphs etc.), and level of detail. The prompt is crafted to guide the model to produce detailed, structured scenarios every time. This ensures higher quality training data.

Example Prompt for Our Fine-Tuned GPT-3 Model

The video demonstrates a sample prompt with 4 variables filled in - USA, 2023, jobs, transportation. This produces a detailed scenario of how AGI in 2023 affects jobs and transportation in the USA. Running the prompt multiple times with different variable combinations generates diverse training data sets for fine-tuning.

Generating Synthetic Training Data Sets

The script in the video uses 4 variables with 4 options each to easily generate 256 combinations. This synthetic data serves as training examples for fine-tuning.

More variables allow creating diverse datasets with fewer options per variable. The video recommends at least 200 examples for fine-tuning a model.

Using Multiple Variables for Diverse Data Sets

The prompt has 4 variables - place, year, parameter 1 and parameter 2. Each variable has 4 options defined in the script. Combining the variables results in 256 unique prompts to generate training data.

Running the Script to Create Hundreds of Examples

The video shows the script being run which generates a new scenario for each variable combination. This results in 256 diverse examples using just 4 options per variable. Adding more options can quickly generate even larger datasets.

Augmenting the Training Data for Quality

It's important to remove low quality examples from training data to improve model performance. The video shows one way to filter the data.

The generated scenarios are sorted by length as a proxy for amount of detail. The shortest, least detailed examples are removed resulting in higher quality data.

Preparing the JSON File to Upload for Fine-Tuning

OpenAI expects training data for fine-tuning to be uploaded in a specific JSON format with prompt and completion pairs.

The video demonstrates a script that matches up prompts and completions to create the properly formatted JSON file for upload.

Uploading the Data and Retrieving the Fine-Tuned Model

Another script is used to upload the training data JSON file and initiate fine-tuning of the GPT-3 model.

Once complete, the fine-tuned model is available in the Playground to test and use.

Testing the Performance of Our Customized Model

The video demonstrates testing the fine-tuned model with a new prompt using variables it wasn't trained on.

The model generates a new detailed scenario based on the provided variables, showing it learned the general structure.

FAQ

Q: How many training examples do you need to fine-tune GPT-3?
A: You need at least a few hundred high-quality training examples, with performance increasing linearly with more data.

Q: What format does the training data need to be in?
A: The training data needs to be in a JSON file format, with each example containing the prompt and corresponding completion.

Q: How expensive is it to fine-tune a GPT-3 model?
A: For a few hundred examples, fine-tuning can cost around $10. The more data you use, the more expensive it becomes.

Q: Can I share my fine-tuned GPT-3 model?
A: Unfortunately you cannot share your personalized fine-tuned models. They are only accessible through your OpenAI account.

Q: How do I upload my training data to fine-tune GPT-3?
A: You can use OpenAI's API or their provided Python script to upload your JSON file for model fine-tuning.

Q: What if my fine-tuned model starts repeating itself?
A: This usually happens when you haven't provided enough quality training data. Try increasing the number of examples to improve performance.

Q: Should I manually vet the training data?
A: Ideally, yes - having human experts validate the quality of examples improves model performance. But synthetic data can also work.

Q: What format should I craft my prompts in?
A: Strive for consistency in structure and include clear variables/parameters so new combinations can be generated.

Q: How do I access my fine-tuned model?
A: Fine-tuned GPT-3 models appear in the 'Fine-Tunes' section of your OpenAI playground account.

Q: Can anyone use my customized GPT-3 model?
A: No, fine-tuned models are restricted only to your account and cannot be shared publicly.