Welcome to the LargeGPT project! This project is an AI-powered chatbot built from scratch by DrDataYE, without utilizing any external API services.

Training The Model

Use The Model LLM

To use LargeGPT on your system, follow these steps:

1. Set up a virtual environment using your preferred environment manager (e.g., virtualenv or conda).
2. Activate the new environment.
3. Install dependencies using the following command:

pip install -r requirements.txt

This repository contains a PyTorch implementation of a Bigram Language Model. The model is based on the Transformer architecture and is designed for text generation tasks. It uses self-attention mechanisms to capture contextual information and generate coherent and contextually relevant text.

1. Clone the repository:

   git clone https://github.com/DrDatYE/LargeGPT.git
   cd LargeGPT

2. Install the required dependencies:

   pip install -r requirements.txt

You can train the Bigram Language Model and use it for text generation using the following steps:

To train the model with custom hyperparameters, you can run the following command:

python train.py --batch_size 32 --block_size 64 --max_iters 2000 --learning_rate 0.001

Adjust the hyperparameters as needed. The training progress and evaluation results will be displayed in the console.

After training the model, you can use it for text generation. To generate text based on a user input, run the following command:

python generate.py --input "Once upon a time in"

Replace the input text with your own text. The generated text will be displayed in the console.

You can customize the model's behavior using various hyperparameters. Here are some of the available hyperparameters that you can configure:

• --batch_size: Batch size for training.
• --block_size: Maximum context length for predictions.
• --max_iters: Maximum number of training iterations.
• --learning_rate: Learning rate for optimization.
• --device: Device for training (cuda or cpu).
• --eval_iters: Number of iterations for evaluation.
• --n_embd: Number of embedding dimensions.
• --n_head: Number of attention heads.
• --n_layer: Number of layers in the model.
• --dropout: Dropout rate.

Feel free to experiment with different hyperparameters to achieve the best results for your specific text generation task.

1. Clone the repository:

   git clone https://github.com/DrDataYE/LargeGPT.git

2. Install the required packages:

   pip install -r requirements.txt

3. Move the files from the LargeGPT directory to the root:

   mv -if LargeGPT/* .

To train the LargeGPT model, run the following command:

python train.py

To use the trained LargeGPT model, run the following command:

python use.py

To download and set up the IMDb dataset, run the following commands:

pip install datasets
python dataset.py -n imdb -o ./data/

If you're interested in training your own GPT model, you can follow these steps:

1. Access the OpenAI website and obtain access to their API.
2. Modify the train.py file to suit your training needs and strategies.
3. Run the program to start the training process:

python train.py

If you're interested in contributing to the development of LargeGPT, we welcome contributions at all levels! Open a new issue to discuss proposed changes or submit a pull request from relevant branches.

This project is licensed under the LIM License. Refer to the LICENSE file for more details.

LargeGPT was developed by DrDataYE. For inquiries, please contact us at drdataye@gmail.com or visit our website https://www.cyber1101.com.