How to Fine Tune ChatGPT-3.5 Turbo Easily

How to Fine Tune ChatGPT-3.5 Turbo Easily

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:

  file=open("mydata.jsonl", "rb"),

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.

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