Issues with Fine-Tuning GPT-3.5 Turbo for Domain-Specific Applications - Unexpected Output Patterns
I'm learning this framework and I'm relatively new to this, so bear with me... I'm upgrading from an older version and I've searched everywhere and can't find a clear answer... I tried several approaches but none seem to work. I'm trying to fine-tune the GPT-3.5 Turbo model using the OpenAI API for a customer service domain. However, after running multiple training iterations with a dataset of around 5,000 examples, I'm seeing unexpected output patterns that don't match the training data. For example, when I ask the model about a specific product return policy, it sometimes provides generic responses unrelated to the specifics I've trained it on. I've set up the training loop using the OpenAI Python library (version 0.27.0), and I'm following the code snippet below: ```python import openai # Set the API key openai.api_key = 'YOUR_API_KEY' def fine_tune_model(training_data): response = openai.FineTune.create( training_file=training_data, model="gpt-3.5-turbo", n_epochs=4, batch_size=16, learning_rate_multiplier=0.1 ) return response # Assuming `training_data` is a file uploaded using OpenAI's File API fine_tune_response = fine_tune_model("file-abc123") print(fine_tune_response) ``` I tried adjusting the `n_epochs` and `learning_rate_multiplier`, but those changes don’t seem to improve the model's ability to generate responses closely aligned with the training examples. I also made sure that the training examples are well-formatted JSONL, and I've included diverse contexts and variations in phrasing. I noticed a warning in the console about the dataset being too small for effective fine-tuning, which seems odd since 5,000 examples should be sufficient for this task. The warning message was: ``` Warning: Dataset may be too small to achieve desired model performance. ``` Could this warning be the root cause? Is there a best practice for preparing the dataset for fine-tuning with domain-specific knowledge? I would appreciate any insights or suggestions for how to improve the performance of my fine-tuned model. This is part of a larger API I'm building. What am I doing wrong? I'm using Python LTS in this project. Any pointers in the right direction? This is happening in both development and production on macOS. Has anyone else encountered this? I'm working in a macOS environment.