Train your own private Large Language Model (LLM)

To install and train the "privateGPT" language model locally, you can follow these steps:

Clone the Repository: Start by cloning the "privateGPT" repository from GitHub. Open your terminal or command prompt and run the following command:

git clone https://github.com/imartinez/privateGPT.git

Set Up Virtual Environment (optional): Move into the cloned repository and create a virtual environment to isolate your project's dependencies. Run the following commands:

cd privateGPT
python3 -m venv venv
source venv/bin/activate

Install Dependencies: Install the required Python dependencies by running the following command:

pip install -r requirements.txt

Download Pre-trained Model: Download the pre-trained weights for the "privateGPT" model. Check the repository's documentation or README file for instructions on how to obtain the model files.

Prepare Training Data: Prepare your training data in a suitable format. The "privateGPT" repository provides an example dataset in the datasets directory. You can use this dataset or replace it with your own data. Ensure your data is preprocessed and tokenized appropriately.

Configure Training Parameters: Open the train.py file in the repository and modify the training parameters as per your requirements. Set the paths to your training data, pre-trained model, and define other hyperparameters such as batch size, learning rate, and number of training steps.

Train the Model: Start the training process by running the following command:

python train.py

The script will load the pre-trained model, train it on your data, and save the updated model weights at specified intervals.

Monitor the Training: During the training process, you can monitor the progress by observing the output in the terminal. It will display information about the current training step, loss, and other relevant metrics.

Save the Trained Model: Once the training is complete or at the desired interval, the model weights will be saved automatically. You can find the saved weights in the models directory.

That's it! You have now installed and trained the "privateGPT" language model locally using the provided repository. Remember to refer to the repository's documentation or README file for any specific instructions or additional steps required for your use case.