This script helps to automate the process of preparing data for finetuning on OpenAI models, specifically GPT-3.5 and Babbage. It also provides utilities to validate the data, transform the data to the required JSONL format, and estimate the cost of the finetuning process.

• Validate API Key
• Validate and select the appropriate model (GPT-3.5 or Babbage)
• Check input data file (JSONL)
• Estimate finetuning cost
• Create and manage finetuning jobs on OpenAI

• Python 3
• External libraries: pyfiglet, openai, tiktoken, dotenv, argparse, json, re, os, sys, time, clint

To install the required libraries:

pip install pyfiglet openai tiktoken python-dotenv argparse clint

or

pip install requirements.txt

python ftup.py [-k <API_KEY>] -m <MODEL_NAME> -f <INPUT_FILE> [-s <SUFFIX>] [-e <EPOCHS>]

Arguments:

• -k, --key: Optional. API key. Optional argument, but required in default env to have an API key in enviroment. OPENAI_API_KEY
• -m, --model: Required. Model to use. Options: gpt for gpt-3.5-turbo-0613 or bab for babbage-002.
• -f, --file: Required. Input data file (JSONL format).
• -s, --suffix: Optional. Add a suffix for your finetuned model. E.g., 'my-suffix-title-v-1'.
• -e, --epoch: Optional. Number of epochs for training. Default is 3.

Store your API key in a .env file in the format:

OPENAI_API_KEY=your_api_key_here

The script will load by default this key if not -k / --key passed as an argument.

• check_key(key): Validates format for OpenAI API key.
• check_model(model): Validates the model name.
• check_jsonl_file(file): Checks if the provided file has a valid JSONL name and if it exists.
• create_update_jsonl_file(model, file): Check if JSONL have a correct format and uploads file to OpenAI.
• update_ft_job(file_id_name, model, suffix, epoch): Creates or updates the finetuning job on OpenAI.
• check_jsonl_gpt35(file): Validates the format for GPT-3.5 training.
• check_jsonl_babbage(file): Validates the format for Babbage-002 training.
• cost_gpt(file, epochs): Estimates the cost of the finetuning process.

• Ensure your data adheres to OpenAI's data format guidelines for finetuning.
• Monitor your OpenAI dashboard to keep track of your usage and costs.

1. OpenAI Documentation
2. OpenAI Cookbook - FineTuning
3. Python Argparse Library
4. pyfiglet Documentation
5. tiktoken Library

• Cancel training pressing key
• Adding Token and cost for Babbage model
• Automate for creating train and validation files 80-20%

$ python ftup.py --key your_api_key_here --file train_gpt3_5.jsonl --model gpt --epoch 1 --suffix custom-model-name
or
$ python ftup.py -f train_gpt3_5.jsonl -m gpt -e 1 -s custom-model-name
    ____________            __  ______ 
   / ____/_  __/           / / / / __ \
  / /_    / /    ______   / / / / /_/ /
 / __/   / /    /_____/  / /_/ / ____/ 
/_/     /_/              \____/_/  


Checking API key ...
- API Key

Checking model ...
- Model gpt

Checking if jsonl is valid ...
- JSON File train_gpt3_5.jsonl

Checking if jsonl format is valid for GPT-3.5 training ...
- Num examples: 225
- JSONL train_gpt3_5.jsonl correct format

Uploading jsonl train file ...
- File ID: file-abcd123

Dataset has ~15153 tokens that will be charged for during training
You'll train for 1 epochs on this dataset
By default, you'll be charged for ~15153 tokens
Total cost: $0.1212 💰

Creating a finetuning job ...
- Fintetuning job id: ftjob-abc123

Status: succeeded
Finetuning succeeded! ☑️
Finetune model: ft:gpt-3.5-turbo:openai:custom-model-name:7p4lURe