LLM Projects

This repository is a collection of mini-projects, notebooks, and experiments focused on Large Language Models (LLMs). It covers various topics including LangChain, Llama models, and integration with services like Together AI and OpenAI.

Table of Contents

• Project Structure
  • LangChain Notebooks
  • Llama Notebooks (DeepLearning.AI)
  • Other Notebooks
  • ML Application
• Setup and Installation
• Environment Variables
• Resources
• License

Project Structure

LangChain Notebooks

These notebooks follow the LangChain for LLM Application Development course and cover:

• langchain-Model_prompt_parser.ipynb: Introduction to Models, Prompts, and Output Parsers.
• langchain-Chains.ipynb: Working with different types of chains (Simple, Sequential).
• langchain-Memory.ipynb: Implementing memory in LLM applications.
• langchain-QnA.ipynb: Question and Answering over documents.
• langchain-Evaluation.ipynb: Evaluating LLM applications.
• langchain-Agents.ipynb: Using LLMs as reasoning engines to take actions.

Llama Notebooks

A series of notebooks focused on Llama models, inspired by DeepLearning.AI's "Prompt Engineering with Llama 2/3" course:

• deeplearning_ai_getting_started_llama.ipynb: Basic interactions with Llama.
• deeplearning_ai_prompt_engineering_techniques_llama.ipynb: Advanced prompting strategies.
• deeplearning_ai_code_llama.ipynb: Using Llama for code generation and analysis.
• deeplearning_ai_multi_turn_conversations_llama.ipynb: Building conversational interfaces.
• deeplearning_ai_comparing_llama_models.ipynb: Comparing different Llama model sizes and versions.
• deeplearning_ai_llama_guard.ipynb: Implementing safety guardrails.
• deeplearning_ai_walkthrough_helper_function.ipynb: Utility functions for Llama API calls.

Other Notebooks

• together_ai_service_setup.ipynb: Setting up and using the Together AI API.
• tokenise_test_gpt2.ipynb: Testing tokenization with GPT-2.

ML Application

• ml_app/: A standalone Flask application for image classification.
  • app.py: Flask API using MobileNetV2 (TensorFlow/Keras).
  • Includes sample images for testing.

Setup and Installation

1. Clone the repository:

   git clone https://github.com/ppant/llm-projects.git
   cd llm-projects

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies: The notebooks require several packages. You can install them using:

   pip install langchain openai together python-dotenv tensorflow flask pillow numpy

Environment Variables

Some notebooks require API keys from OpenAI or Together AI. Create a .env file in the root directory and add your keys:

OPENAI_API_KEY=your_openai_api_key_here
TOGETHER_API_KEY=your_together_api_key_here

Resources

• DeepLearning.AI - Prompt Engineering with Llama 2
• Together AI API Documentation
• LangChain Documentation

License

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