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gpt-3-whatsapp-training

A project that implements GPT-3 training with whatsapp messages.

How it works?

This simple Python script gets your Whatsapp conversations exported as TXT and compress into a single jsonl file which is used to train a fine-tuned Open AI GPT-3 model, to simulate your responses.

Requirements

  • OpenAI account and OpenAI API Token
  • Python 3.11
  • Few whatsapp conversations (More than 2000 messages)
  • Around $20 to train and use the fined-tuned model

Usage

  • Create OpenAI API account
  • Get the API Key
  • Download and install Python 3.11
  • Clone this repo git clone https://github.com/felps-dev/gpt-3-whatsapp-training
  • Create a virtualenv inside the cloned repo python3 -m venv env
  • Activate your virtual env env/Scripts/Activate(for windows) and source env/bin/activate for unix based OS (Mac, Linux, etc)
  • Install requirements pip install -r requirements.txt
  • Export your Whatsapp messages
    • Go to a conversation
    • Press 3 dots on top right corner
    • Export conversation
    • Export without media
    • Get the TXT file from each conversation
  • Put the TXT files from previous step inside training_data folder
  • Create a copy of the file .env_template named as .env
  • Fill the .env with your info
    • RESPONSE_USER - Is your nickname on TXT files
    • OPENAI_API_KEY - Is the api key from previous steps
    • MODEL_ENGINE - Is the model generated after training finishes
  • Don't worry to not have MODEL_ENGINE, you didnt made the training yet.
  • Prepare the data for training using python3 compress_training_data.py
  • This will create a new file named training_data.jsonl
  • Start the training using the command openai api fine_tunes.create -t training_data.jsonl -m davinci
  • You can use other models instead davinci, see there
  • Wait until the training finishes, you can get the info using openai api fine_tunes.get -i your_ft_id_given_by_previous_command
  • Once the training finishes, you can get your model using the info from previous step, on fine_tuned_model property.
  • Now put your model-id inside MODEL_ENGINE at .env file
  • Start chat by using python3 chat.py