Posts

Recently I was asked to come up with a solution to classify a large list of tens of thousands of email addresses based on their details. Although the original request was a bit different, for this blog post I will make the example of classifying the addresses as being from a personal email provider (like Gmail, Yahoo, etc.) or from a business or other institution. I will demonstrate an approach how to use a self-hosted Zephyr-7B model using the Litserve package to perform this task very efficiently.

Nowadays it feels that the TrueNAS CORE community is shrinking every month. I have been a user of this solution for more than 8 years now, but some aspects of the system like plugins have never matured to a degree where they are really usable.

Last November ChatGPT has held its first anniversary, after having reached a user base of 180 million active users, making this the fastest adopted technology with an internet user base ever. Now we are a year in, I’d like to explore how the open-source world has caught up with ChatGPT’s success. What is the current state of affairs when comparing open source LLMs to the closed source and proprietary models from OpenAI and others? Is open-source technology able to match or surpass the technology brought by OpenAI in 2022?"

Retrieval Augmented Generation (RAG) allows for adding knowledge from external sources into a Large Language Model (LLM) prompt. In this blog post I will be using this approach for using an LLM to retrieve data from an SAP S4/HANA system and visualizing the data using Streamlit. You will learn how to build a dashboard converting natural language questions into data visualizations using only a minimal amount of code!

With the Tour de France 2023 having started last weekend there will be lots of media coverage of the world’s toughest cycle race in the next weeks. If you are a regular viewer of these races you will probably recognize a few situations where you found it difficult to exactly determine who you are watching on the screen.

GPT-based models have several inherent problems such as correctly aggregating figures in tabular structures and getting access to corporate data in database system. I will show an approach for connecting GPT to your custom database schema using the Langchain Python package. This allows text-based queries to be executed on the custom database schema, showing impressive results in GPTs ability to formulate complex SQL queries to the database and retrieving the correct information.

One of the major drawbacks of GPT-based models is their lack of knowledge about custom documents living in knowledge bases in large organizations. In this blog post I will demonstate a way to set up adding custom documents into the body of knowledge of GPT-based models.

Large language models are emerging as a tool for solving many information and knowledge-related matters. Back in 2020 GPT-3 made huge improvements in the way that humans are able to interact with an AI, where the model showcased its ability to engage in compelling conversations with a human counterpart. In this article I will showcase how these large language models can be used for answering questions from data stored in tabular form, just as the majority of the world’s business data.

In December 2022 SAP held a machine learning competition called “I quit!” attended by 50 participants to showcase the machine learning capabilities of SAP HANA Cloud. The idea is to predict from a database of employees who is the most probable to be leaving his or her job in the short term, based on historical information of employees which have quit recently.

In my last blog post I have shown how to download the Stable Diffusion model weights and set up an image generation workflow yourself using the Stable Diffusion model as provided by the Huggingface Transformers library.