OReilly Live-Training: "Getting Started with Llama3"

Repository for the oreilly live training course: "Getting Started with Llama3": https://learning.oreilly.com/live-events/getting-started-with-llama-2/0636920098588/

Setup

Conda

• Install anaconda
• This repo was tested on a Mac with python=3.10.
• Create an environment: conda create -n oreilly-llama3 python=3.10
• Activate your environment with: conda activate oreilly-llama3
• Install requirements with: pip install -r requirements/requirements.txt
• Setup your openai API key

Pip

1. Create a Virtual Environment: Navigate to your project directory. Make sure you hvae python3.10 installed! If using Python 3's built-in venv:

   python -m venv oreilly-llama3

   If you're using virtualenv:

   virtualenv oreilly-llama3

2. Activate the Virtual Environment:

   • On Windows:

     .\oreilly-llama3\Scripts\activate

   • On macOS and Linux:

     source oreilly-llama3/bin/activate

3. Install Dependencies from requirements.txt:

   pip install python-dotenv
   pip install -r requirements/requirements.txt

4. Setup your openai API key

Remember to deactivate the virtual environment once you're done by simply typing:

deactivate

Setup your .env file

• Change the .env.example file to .env and add your OpenAI API key.

To use this Environment with Jupyter Notebooks:

• pip install jupyter
• python3 -m ipykernel install --user --name=oreilly-llama3

Notebooks

Here are the notebooks available in the notebooks/ folder:

1. Getting Started with Llama 2

   

2. Hello Llama Local

   

3. Ollama Quick Setup

   

4. Query Docs with Llama 2

   

5. LangChain Llama2 QA CSV

   

6. Quiz from Source Llama2 - Llama Index

   

7. Structured Output Langchain Llama2

   

8. Fine Tuning Llama2

   

9. Quantization Basics

   [](https://colab.research.google.com/github/

10. Fine Tuning Llama2