Have you ever wished your AI chatbot could better understand and assist with your specific needs?
Well, the good news is, it can! Welcome to the world of fine-tuning ChatGPT 3.5 Turbo.
It’s like teaching your chatbot new tricks, customizing it to be a perfect fit for your applications.
In this guide, we’ll walk you through the exciting journey of fine-tuning, so you can empower your ChatGPT to be an even more valuable.
Here are the steps to fine-tune your AI:
Step 1: Preparation
Before diving into fine-tuning, make sure you have:
- Training Data: Gather real-life examples of conversations or tasks that you want your AI to excel at. Ensure they are in the right format.
Step 2: Data Formatting
Format your training data correctly. For gpt-3.5-turbo, use a conversational format with messages, roles, and content. For other models, you can use a prompt-completion pair format.
Step 3: Dataset Size
Prepare at least 10 examples, but for better results, aim for 50 to 100 examples. The ideal number varies depending on your use case.
Step 4: Token Limits
Each training example is limited to 4096 tokens. Make sure your examples fit within this limit.
Step 5: Cost Estimation
Estimate the cost of your fine-tuning job using the formula:
base cost per 1k tokens * number of tokens in the input file * number of epochs trained
Step 6: Validate Data
Use OpenAI’s data formatting script to check your data for potential errors, token counts, and cost estimates.
Step 7: Upload Data
Upload your prepared data using the OpenAI API:
openai.File.create(
file=open("mydata.jsonl", "rb"),
purpose='fine-tune'
)Step 8: Start Fine-Tuning Job
Initiate the fine-tuning job using the OpenAI SDK:
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
openai.FineTuningJob.create(training_file="file-abc123", model="gpt-3.5-turbo")Step 9: Wait for Completion
Wait for the fine-tuning job to complete. It may take some time, depending on the model and dataset size.
Step 10: Use Your Fine-Tuned Model
Once your job is done, you can use the fine-tuned model for your specific tasks. Make requests to it using the API and specify the model name.
That’s it! You’ve successfully fine-tuned your AI, and now it’s ready to provide even better results for your specific applications.