LoRA (Low-Rank Adaptation) and fine-tuning are two methods to adapt large language models (LLMs) to specific tasks or domains. LLMs are pre-trained on massive amounts of general domain data, such as GPT-3, RoBERTa, and DeBERTa, and have shown impressive performance on various natural language processing (NLP) tasks. Why fine tune a LLM? Fine-tuning of LLMs...
Author: Martin Treiber
Harnessing the Power of Large Language Models for Next-Gen Applications
The realm of Large Language Models (LLMs) has been expanding with a notable trend towards open-source models or their close counterparts. With more models now available under user-friendly licenses, developers are bestowed with a broader spectrum of tools for crafting applications. In this blog post, we explore the diverse methodologies to leverage LLMs, ranked from...
AI engineer: a novel role for the emerging LLM Stack
The world of technology is no stranger to evolution, but every so often, it encounters a juncture that goes beyond mere refinement, signaling a profound metamorphosis. As we traverse the 2020s, we are witnessing one such monumental shift: the AI-enabled application ecosystem is not only maturing but is also reshaping the very foundation of our...
The Rapid Evolution of Enterprise AI Adoption
In a little more than three years, we have witnessed AI evolving from a niche domain to mainstream, with LLMs playing a significant role in this transformation, especially with the introduction of Large Language Models (LLMs) like ChatGPT. 1. The ChatGPT Phenomenon ChatGPT, a revolutionary LLM, garnered over 100 million users in a mere three...
A Deep Dive into OpenAI’s ChatGPT and its Plugins
ChatGPT, the chatbot developed by OpenAI, is known for its ability to produce complex and eloquent texts. However, despite this performance, the generated texts can sometimes be incorrect or misleading in content. The chatbot’s processing is based on the probability distribution of words, without any real knowledge or understanding of the content. Its main function...
The Secrets of GPT-4 Leaked?
In a recent development, internal secrets of OpenAI’s GPT-4 have been leaked. This event has sparked discussions across the artificial intelligence community, given that GPT-4 is a significant progression from its predecessor, GPT-3, in terms of both size and complexity. The advancement in the model’s structure and scale is noteworthy, indicating a new phase in...
Ghosts in the AI machinery
Ever wonder how (generative) AI gets so smart? It’s not just algorithms and code. It’s the work of human annotators, the ghosts in the machine, people who sift through mountains of raw data, categorizing and labeling it, all to train the machines we’ve grown to depend on. This ‘ghost’ isn’t an ethereal presence but a...
AI Evolution and Jobs: is it this Time Different?
The anxiety around job loss due to automation, mechanization, AI, and other technological advancements has indeed been a persistent concern. Since the advent of machines like the mechanical loom, the fear of technology taking over human labor has lingered. It’s true that every major technological leap so far has led to an increase in jobs...
OpenAI Announces Function Calling and Other API Updates
OpenAI has recently announced a series of updates to their API models, including more steerable API models, function calling capabilities, longer context, and lower prices. These updates are designed to enhance the capabilities of the models and provide developers with more flexibility and control. One of the most significant updates is the introduction of function...
Super Apps vs. Large Language Models
In the rapidly evolving world of software, Super Apps and Large Language Models (LLMs) offer two distinct approaches to address user needs and preferences. Super Apps, like WeChat, consolidate various functions and services into a single platform, while LLMs, such as GPT from OpenAI promise a new era of software adaptability through self-modifying code generation....
