Large Language Model (LLM) Welcome to the Large Language Model (LLM) repository! This repository contains code for training and using a large language model based on recurrent neural networks (RNNs) and transformer architectures.

Overview The Large Language Model is designed to generate text based on the patterns and structures learned from a given dataset. It can be used for various natural language processing tasks such as text generation, language translation, and sentiment analysis.

Features Train a language model on custom text data Fine-tune pre-trained language models for specific tasks Generate text based on user input or prompts Evaluate the performance of the language model using metrics such as perplexity Getting Started To get started with the Large Language Model, follow these steps:

Clone the repository to your local machine: bash Copy code git clone https://github.com/anesu398/Large-Language-Model.git Install the required dependencies: Copy code pip install -r requirements.txt Prepare your text data in a suitable format (e.g., a text file). Train the language model using the provided scripts or notebooks. Use the trained model to generate text or perform other natural language processing tasks. Usage To use the Large Language Model for text generation, follow these steps:

Load the trained model and vocabulary: python Copy code

Load the model

model = load_model('language_model.h5')

Load the vocabulary

vocab = np.load('vocab.npy', allow_pickle=True).item() Generate text using the model: python Copy code

Generate text

input_text = "The quick brown fox" generated_text = generate_text(model, vocab, input_text, length=100) print(generated_text) Contributing Contributions to the Large Language Model project are welcome! If you have ideas for improvements, bug fixes, or new features, feel free to open an issue or submit a pull request.

License This project is licensed under the MIT License - see the LICENSE file for details.