Skip to content

Latest commit

 

History

History
46 lines (35 loc) · 1.73 KB

README.MD

File metadata and controls

46 lines (35 loc) · 1.73 KB

TinyLLM

Description

TinyLLM is a lightweight implementation of a decoder model, along with a tokenizer, using PyTorch. This project was inspired by Andrej Karpathy's lecture on language modeling.

Table of Contents

Description

TinyLLM aims to provide a simple and accessible implementation of a decoder model for natural language processing tasks. It utilizes PyTorch, a popular deep learning framework, to build and train the model.

Installation

To install and set up TinyLLM, follow these steps:

  1. Clone the repository:

$ git clone https://github.com/anirudhr20/tiny-llm.git

  1. Install the required dependencies:

$ pip install -r requirements.txt

Usage

To use TinyLLM in your own projects, follow these guidelines:

  1. Import the necessary modules:
  • $ from llm.model import TinyLLM
  • $ from llm.model import TinyTokenizer

  1. Training the model: cd Scripts/ python train.py

  2. Model Inference: cd Scripts/ python inference.py

Contributing

Contributions to TinyLLM are welcome! If you would like to contribute, please follow these steps:

  1. Fork the repository: https://github.com/anirudhr20/tiny-llm.git
  2. Create a new branch for your feature: $ git checkout -b feature-name
  3. Make your changes and commit them: $ git commit -m "Add feature-name"
  4. Push your changes to your forked repository: $ git push origin feature-name
  5. Open a pull request on the main repository.

License

TinyLLM is distributed under the MIT License. See the LICENSE file for more information.