Stable first steps towards LLMs

Description

This repository contains the source code for getting started with LLMs. The project is developed in Python and leverages advanced language models to generate descriptive text based on user-defined topics and word counts.

Features

• Generate descriptive text on any topic within a specified word count.
• Utilize pre-trained language models from the Hugging Face model hub.
• Accept command-line arguments for topic selection and word count customization.
• Easily customize prompts and language model configurations for specialized use cases.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

• Python (version 3.12)
• Pipenv

Installation

1. Clone the repository:

   git clone https://github.com/guptaachin/learn-llmchain.git

2. Navigate to the project directory:

   cd learn-llmchain

3. Run the run.sh script:

   • On Linux:

     ./run.sh

   • On Windows (using Git Bash or similar):

     bash run.sh

4. Follow the on-screen instructions to install Pipenv if it's not already installed.

Usage

1. After running the run script, activate the virtual environment:

   pipenv shell

2. Follow the additional instructions provided after activating the virtual environment.

Additional Instructions

• Create a Hugging Face API token. You can create one here.

• Create a .env file in the current directory. Add your Hugging Face API token to the .env file as HUGGINGFACE_TOKEN=your_token_here.

• Run your Python application using:

  python main.py --topic life --length 5

License

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

References

• Langchain-docs
• Hugging Face