| marp | theme | class | size | style | header | footer |
|---|---|---|---|---|---|---|
true | default | invert | 14580 | img {background-color: transparent!important;} a:hover, a:active, a:focus {text-decoration: none;} header a {color: #ffffff !important; font-size: 40px;} footer {color: #148ec8;} | [◇](#1 " ") | Simon Chen 2023 |
markedown drawing UML (Unified Modeling Language) diagrams
- 1-train.js: generate nomnoml based on work instruction text
- 2-train.js: generate text based on nomnoml input
- 3-openFDA.js: data preparation
.. base model + training
- base model: GPT-4 or GPT-3.5 turbo
- training: data: secured repository (Azure Cloud) alogrithm: +vectorstore embedding +finetuinging (prompt engineering) +nascent tools
(eg BLIP2, Salesforce)
data: mock wi-320 Tesla Maintenance Manual (https://onedrive.live.com/?cid=597A1F50B291367A&id=597A1F50B291367A%216571&parId=597A1F50B291367A%216234&o=OneUp) 
vlidation_tesla
-- multiple iterations / multiple epochs
-
FDA API endpoint
https://api.fda.gov/device/event.json?search=device.generic_name:tomography&limit=1 -
Intervalize
// Poll OpenFDA every 60 minutes setInterval(checkAdverseEvents, 60 * 60 * 1000);





