https://github.com/gjbex/ai-tools

Material for a training on AI tools
https://github.com/gjbex/ai-tools

artificial-intelligence artificial-neural-networks deep-learning deployment llama llm machine-learning machine-learning-algorithms training training-materials

Last synced: about 2 months ago
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Material for a training on AI tools

• Host: GitHub
• URL: https://github.com/gjbex/ai-tools
• Owner: gjbex
• License: cc-by-4.0
• Created: 2024-07-19T14:57:56.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-12-12T11:31:23.000Z (about 2 months ago)
• Last Synced: 2024-12-12T12:29:03.673Z (about 2 months ago)
• Topics: artificial-intelligence, artificial-neural-networks, deep-learning, deployment, llama, llm, machine-learning, machine-learning-algorithms, training, training-materials
• Language: HTML
• Homepage: https://gjbex.github.io/AI-tools/
• Size: 18.4 MB
• Stars: 3
• Watchers: 1
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        
# AI tools

This is a repository that illustrates the use of

various AI tools and techniques.

## What is it?

1. `local_LLMs.pptx`: PowerPoint presentation on running Large Language

   Models (LLMs) on a local machine.

1. `source-code`: directory with the source code.

1. `models`: directory with scripts to download pre-trained models.

1. `data`: directory with scripts to download data.

1. `tools`: directory with tools to run LLMs on a local machine.

1. `docs`: directory for a web site on this training.

1. `CONTRIBUTING.md`: guidelines for contributing to this repository.

1. `LICENSE`: license information for this repository.

1. `CODE_OF_CONDUCT.md`: code of conduct for this repository and training.

## Conda environments

Since conda environments for machine learning have many dependencies, we

opted to have a separate environment for each directory in `source-code`,

rather than one environment for all code.  This makes it easier to manage

dependencies, and to avoid conflicts between different packages that may

be required by different scripts.