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AA

LLM Model Training

This repository contains the code for training a simple LLM model using PyTorch.

Requirements

  • Python 3.7 or higher
  • PyTorch 1.9.0 or higher

Installation

  1. Clone this repository.
  2. Install the required dependencies by running pip install -r requirements.txt.

Running the Model

  1. Update the main.py file with your desired input size, output size, and initial state for the Game class.
  2. Run the main.py file: python main.py. This will train the LLM model and save it as saved_model.pt.

Loading the Trained Model

  1. Initialize an LLM model with the appropriate input and output sizes.
  2. Load the saved model's state dictionary using llm.load_state_dict(torch.load("./saved_model.pt")).

###< saved_model: The saved_model.pt file is generated when you save the trained LLM model using torch.save(llm.state_dict(), "./saved_model.pt"). You don't need to create this file manually; it will be created when you run the training script (main.py).

l Step by step guide on how to run the model from GitHub:

Clone the GitHub repository to your local machine: git clone xxx

Change into the repository's directory: bash cc: cd your_repository Install the required dependencies:

cc: pip install -r requirements.txt Update the main.py file with your desired input size, output size, and initial state for the Game class. Run the training script:

css cc: python main.py This will train the LLM model and save it as saved_model.pt.

To load and use the trained model in another script or project, follow these steps: a. Initialize an LLM model with the appropriate input and output sizes.

b. Load the saved model's state dictionary:

python cc: llm.load_state_dict(torch.load("./saved_model.pt")) Now you can use the trained model for making predictions or further fine-tuning.