What the Heck is GPT-3.5 Fine Tuning?

Aug 24, 2023

OpenAI's GPT-3.5 Turbo has emerged as a leading chat-based LLM model. But did you know you can fine-tune it for even better results? Dive into this guide to understand the ins and outs of GPT-3.5 fine-tuning.

Understanding Fine-Tuning

What is Fine-Tuning?

Fine-tuning is the art of adapting a pre-trained model, like GPT-3.5 Turbo, to cater to specific tasks or domains. Think of it as giving your AI a masterclass in a particular subject. By putting together a dataset of specific instructions and answers, you can train the model to enhance its performance.

Why Fine-Tune?

Enhanced Performance: With just a little tuning data, you can achieve remarkable results.
Shortened Prompts: No more lengthy instructions. Get straight to the point.
Customized Outputs: Want your AI to respond in a Shakespearean tone or output in JSON format? Fine-tuning makes it possible.

How To Fine-Tune GPT-3.5 Turbo?

1. Preparing Your Data

Start by creating specific instructions. For instance:

{
  "messages": [
    { "role": "system", "content": "You are an assistant that occasionally misspells words" },
    { "role": "user", "content": "Tell me a story." },
    { "role": "assistant", "content": "One day a student went to schoool." }
  ]
}

2. Upload the Data

Thanks to OpenAI, you don’t need your own GPU. Simply upload your data using OpenAI's API:

curl -https://api.openai.com/v1/files \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -F "purpose=fine-tune" \
  -F "file=@path_to_your_file"

3. Initiating a Fine-Tuning Job

Kickstart the process with:

curl https://api.openai.com/v1/fine_tuning/jobs \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
  "training_file": "TRAINING_FILE_ID",
  "model": "gpt-3.5-turbo-0613",
}'

4. Start using:

Once fine-tuned, your model is ready to outperform its previous capabilities. You can start using it by just changing the model name in your OpenAI model calls.

Cost Implications of Fine-Tuning

Fine-tuning is not just about performance; it's also cost-effective:

Training: $0.008/1K Tokens
Usage Input: $0.012/1K Tokens
Usage Output: $0.016/1K Tokens

A GPT-3.5-Turbo fine-tuning job with a training file of 100,000 tokens that is trained for 3 epochs would have an expected cost of $2.40.

When compared to GPT-4 models, GPT-3.5 Turbo is a clear winner in terms of cost. And according to OpenAI, a fine-tuned GPT-3.5 Turbo can even outperform GPT-4. Currently, only the 4K tokens model is available.

Should You Fine-Tune?

The decision to fine-tune hinges on your specific needs:

If your current prompts aren't cutting it, or you desire a specific output, fine-tuning is your best bet.
Waiting for the 16K model or even GPT-4 this fall? If you need context beyond 4K tokens or you need GPT-4, patience might be key.
Satisfied with your current application? Stick with it until you're ready to level up.

Here’s a tweet showcasing the prowess of fine-tuned GPT-3.5 Turbo over GPT-4:

https://twitter.com/jerryjliu0/status/1694370574808887496?s=20

GPT-3.5 fine-tuning is a game-changer. Whether you're looking to enhance performance, save on costs, or achieve specific outputs, this guide has you covered. Dive into the world of fine-tuning and unlock the full potential of your AI.

If you found this guide on GPT-3.5 fine-tuning insightful, there's a lot more where that came from! The world of artificial intelligence is rapidly evolving, and I’m here to keep you updated every step of the way.