LLM for Healthcare (llm-healthcare)

This repository contains code and resources related to building and using Large Language Models (LLMs) in the healthcare domain. It includes examples ranging from basic PyTorch implementations to advanced transformer models and guidelines for constructing your own LLMs, potentially using a local server like the one indicated in the image (http://localhost:8891).

Repository Structure

The repository is organized as follows:

• /notes: This directory contains notes, documentation, or experimental code.
• App-A-Pytorch-Basic.ipynb: A Jupyter Notebook demonstrating basic concepts of LLMs using PyTorch. This is a good starting point for understanding the fundamentals.
• App-B-Transformer-Basic.ipynb: A Jupyter Notebook introducing transformer models, which are the foundation of many modern LLMs.
• App-C - Building LLM.ipynb: A Jupyter Notebook that guides you through the process of building your own LLM. This is likely a more advanced tutorial.
• LICENSE: The license file specifying the terms under which the code is distributed.
• myDissInPlainText.txt: text of a dissertation for basic LLM trainig
• utilities.py: A Python file containing utility functions used across the project.

Getting Started

1. Prerequisites:
   • Python 3.x
   • Jupyter Notebook or Jupyter Lab
   • Required Python packages (likely listed in a requirements.txt file, not shown in the image). You can usually install them using pip install -r requirements.txt.
2. Clone the repository:

   git clone <repository-url>
   cd llm-healthcare

3. Explore the Notebooks:
   • Start with App-A-Pytorch-Basic.ipynb to understand the basic concepts.
   • Move on to App-B-Transformer-Basic.ipynb to learn about transformer models.
   • Finally, explore App-C - Building LLM.ipynb for a hands-on experience in building an LLM.

Key Concepts

This repository explores several important concepts related to LLMs in healthcare:

• PyTorch: A popular deep learning framework used for building neural networks.
• Transformers: A neural network architecture that has revolutionized natural language processing (NLP) and is particularly well-suited for building LLMs.
• Large Language Models (LLMs): Powerful AI models capable of understanding and generating human-like text. They have various applications in healthcare, such as:
  • Clinical note summarization
  • Medical question answering
  • Patient-doctor communication assistance
  • Drug discovery and development
  • And much more!

Notes about running LLMs locally

The image suggests that a local server might be used (http://localhost:8891). Please note that running and fine-tuning LLMs can be computationally expensive. You might need significant processing power (e.g. GPUs) and memory to work with larger models. Make sure to check the notebooks for details on any hardware or software requirements related to running the code on your own machine.

License

This project is licensed under the terms of the [LICENSE NAME] license. See the LICENSE file for details.

Acknowledgments

• Add any acknowledgements here if your project is based on or inspired by other work.

Contact

• If you have any questions or need further assistance, you can add your contact information here. For example:
  • For questions about this project, please open an issue on this repository or contact [Your Name] at [Your Email].