Example tutorials and data for the macOS application Reexpress one, which brings reliable and interpretable LLMs directly to your Apple silicon Mac.
For Tutorial 1: Document Classification & Semantic Search (YouTube) see the Tutorial 1 Data Readme.
For Tutorial 2: Adding Uncertainty Estimates to a Generative Language Model (YouTube) see the Tutorial 2 Data Readme.
For "Tutorial 3: Comparing Reexpress to Fine-tuning a Generative AI Model for Classification" see the Tutorial 3 Readme.
For "Tutorial 4: Semantic Search without Labeled Documents" see the Tutorial 4 Readme.
For Tutorial 5: Taming the arXiv Deluge with a Personalized Article Recommender (YouTube) see the Tutorial 5 Readme.
For Tutorial 6: Combine Reexpress with Mixtral-8x7B via Apple's MLX framework (YouTube) see the Tutorial 6 Readme.
For Tutorial 7: Reexpress is your reliable co-pilot for enterprise and professional AI-augmented data analysis (YouTube) see the Tutorial 7 Readme.
For "Tutorial 8: Reexpress+GPT-4 unlocks the potential of AI for legal professionals" see the Tutorial 8 Readme.
These tutorials will help you jump start you data analysis journey, which includes dense (vector) matching, semantic searches, auto visualizations, uncertainty quantification, and more...all without writing a single line of analysis code nor messing with tricky GPU drivers nor sending your data to an external server!
The code in this repo is Apache 2.0 licensed.
If you use Reexpress in your academic research, feel free to use the following citation. More importantly, we'd love to hear from you and feature your work!
@software{reexpress2023,
author = {Reexpress AI},
title = {{Reexpress}: Introspectable, Updatable, and Uncertainty-aware AI via Similarity-Distance-Magnitude data partitions and calibration},
url = {https://re.express},
version = {1.0},
year = {2023},
}