If you’ve found your way here, you’re probably already excited about the potential of the RAGFlow project and eager to see it in action. I was in the same position, so I delved into the codebase to see how it could work with custom large language models (LLMs). This post will walk you through my findings and show you how to get RAGFlow running with your own LLM.
Predictive underwriting is revolutionizing the insurance industry by harnessing data analytics and machine learning. This innovative approach streamlines risk assessment, enhances customer experience, and tackles critical challenges like non-disclosure. By analyzing vast amounts of data from various sources, including wearable devices, insurers can make faster, more accurate decisions. This leads to quicker approvals, fairer pricing, and personalized products. As the insurance landscape evolv...
A Singapore PR Application Profile Evaluator built with Vue.js
This is a modified readthedown rmarkdown template, which is greatly inspired and modified based on juba/rmdformats package. readthedown offer a similar sphnix style, which is commmonly used in various python package documentations. I personally very much like the readthedown style and hence dive a little bit on the source code to figure out ways to make it easier for further customization.
A brief guide on how to deploy deep learning model in browser using tensorflow.js.In this post, a mobileNet model was trained to predict BMI, Age and Gender. The model takes input (either from webcam or uploaded files) to make prediction from browser. This deployment has a obvious advantage of reduced uploading traffic compared to RESTful API approach.