Double Feature: IPyWidgets + Using LLM

Speaker: Fabien Mathieu (Swapcard).
Webpage (with the notebook): www.lincs.fr/events/faites-to....
IPyWidgets is a popular Python library for creating interactive, customizable user interfaces in Jupyter Notebooks and other environments using the power of the Widgets system from the Interactive Computing and Visualization Toolkit (IPython).
Large Language Models (LLMs) are advanced artificial intelligence models capable of generating human-like text based on the input they receive. These models have gained significant attention in recent times due to their ability to understand context, generate creative responses, and assist with various tasks such as text generation, summarization, translation, and more.
In this tutorial, we’ll explore how to create interactive data visualizations using IPywidgets, a popular Python library for building dynamic user interfaces, and Large Language Models (LLMs) like Hugging Face Transformers. By combining these two powerful tools, you can create engaging data visualizations that respond to user input in real-time.
Prerequisites:
- Basic understanding of Python programming
- Familiarity with Jupyter Notebook or JupyterLab environment
Preparation:
- Install IPywidgets if it is not already in your distribution
- Install llama-cpp-python with server option
- Details: github.com/abetlen/llama-cpp-...
- If you have a Cuda card, try to install with cuBLAS option
- Choose and download a GGUF model from Hugging Face
- Small configurations: huggingface.co/TheBloke/phi-2...
- Got a decent Cuda card? huggingface.co/TheBloke/Mistr...
- Look the size and required RAM to choose a specific (sub)version
- Files are in the “Files and versions” tab
Learning Objectives:
- Understand the basics of IPywidgets and Hugging Face Transformers
- Learn how to create interactive data visualizations using IPywidgets
- Discover how to integrate LLMs with IPywidgets to create dynamic visualizations that respond to user input
- Explore examples of practical applications of these techniques
The description above was generated using the assistance of a Mistral-7B-Instruct-v0.2-GGUF model 😉