Jarvis with a Local LLM (OLLAMA)

Hello, everyone! I'm excited to announce our latest tutorial where we dive deep into localizing Concept Bytes' Jarvis using Llama models. Whether you're new to the series or have been following along, this tutorial is packed with valuable insights and practical steps to get you up and running with your own localized Jarvis.

Huge shoutout to @Berle for putting this tutorial together and for assisting everyone on the Discord server.

🌟 What You’ll Learn:

• Introduction to Llama Models: Understand how to integrate and utilize Llama models within the Concept Bytes' Jarvis framework.

• Code Breakdown: We walk you through the code, explaining each part in detail, so you can confidently modify and expand upon it.

• Step-by-Step Localization: Learn how to adapt Jarvis to work with various language models, making it versatile and customizable for your needs.

🛠 Getting Started:

Before jumping into this tutorial, I highly recommend checking out our earlier Jarvis tutorials. This tutorial builds on that foundational knowledge, using code and concepts introduced earlier in the series. If you're new here, don't worry! The previous tutorials will guide you through everything you need to know.

Get the code:

https://github.com/Concept-Bytes/Jarvis

You will need to select between using assist.py and the new assist_local.py. You can change this in Jarvis.py

📝 What You’ll Need to Get Started:

• Python: Make sure you have Python installed on your system.

• Basic Knowledge of Python: Understanding of basic Python programming is essential.

• Access to Llama Models: Ensure you have access to the Llama models or any other language models you'd like to use.

• Concept Bytes' Previous Tutorials: Reviewing the earlier tutorials on Jarvis will be beneficial.

• A Text Editor or IDE: Use your preferred environment for writing and running Python code.

• Internet Connection: For downloading any dependencies or models you may need.