GPT-3 is a powerful language model developed by OpenAI that can generate high-quality text in a variety of styles and domains. However, to use GPT-3 effectively for specific tasks, it may be necessary to fine-tune the model on a specific dataset.
In this guide, we'll cover the basic steps for fine-tuning GPT-3 and provide some tips and best practices along the way.
The first step in fine-tuning GPT-3 is to gather and prepare your data. Depending on the task you're trying to accomplish, this may involve collecting a dataset of text documents, creating a set of labeled examples for a classification task, or curating a set of conversational exchanges for a chatbot. The data can be in the form of CSV, TSV, XLSX, JSON or JSONL file
GPT will be trained by using the data collected in the JSONL files. However, Open AI has tools to tranform CSV, TSV, XLSX, JSON files to JSONL format.
Firstly, you have to type in the folloing code to initialize it.
pip install --upgrade openai
If you are using python, pandas package is mandatory. And the Open AI API key is inevitable as well,
export OPENAI_API_KEY="<OPENAI_API_KEY>"
OPENAI_API_KEY can be checked in your personal account of Open AI.
After that, we can use the following code to transform the CSV, TSV, XLSX, JSON files to JSONL format.
openai tools fine_tunes.prepare_data -f <LOCAL_FILE>
For instance if your data is stored in a CSV file named <LOCAL_FILE.CSV>, after using the code above, it will generate a file named <LOCAL_FILE_prepared.jsonl>.
If we get our JSONL file, then we can start training the model by using the following code.
openai api fine_tunes.create -t <TRAIN_FILE_ID_OR_PATH> -m <BASE_MODEL>
In this case,
openai api fine_tunes.create -t LOCAL_FILE_prepared.jsonl -m davinci
You have to wait for several minutes to generate the new model. If you data sample is very small, the performance of the new model will not be ideal. Eventually, we get a code like the following:
openai api completions.create -m davinci:ft-personal-<> -p