Hello PH community Udit this side from IIT-Patna. 2 weeks ago, we introduced our open-source instant crawling algorithm - HyperCrawl. Today, we are glad to introduce our language model architecture HyperHRT. This is a ML model architecture that utilises serverless fine-tuning along with our in-house developed scraping and summarisation algorithm. Think of this as a single way to fetch real-time real-time information from the web and building a dense vector database over it and further generating human-like responses with it. This is the 2nd generation of LM architecture designed our our lab at the Indian Institute of Technology, Patna. More over, you can enhance the capabilities of any small language model like Microsoft's Phi to beat top models like GPT-4o, Gemini-1.5, etc. This is an experimental phase of our product and please note, only subject-matter experts are invited this this release. Through this release, if you want to access our product, you are needed to be an expert in certain field that can be verified. Users accepted for this release will be granted up to $500 worth of credits to play around and work with the AI for free. We expect to work with 50 subject-matter experts through this release. Fingers crossed!! Regards, Udit A.

Hi everyone, We’re thrilled to introduce HyperHRT - Instant Serverless Finetuning, a groundbreaking LLM alternative that offers instant and real-time fine-tuning capabilities. Developed at the Indian Institute of Technology, Patna, HyperHRT brings a new generation of language model architecture designed for seamless integration with small-language models. We understand the challenges industries face with outdated, inefficient models that require extensive time, money, and computational resources. HyperHRT addresses these issues head-on with its zero-latency real-time retrieval for AI models, ensuring a streamlined and efficient workflow. We're excited to hear your thoughts and feedback. Thank you for checking out HyperHRT! Best regards, Adarsh Prakash

congrats on the launch of hyperhrt. it's intriguing to see the integration with small-language models. could you elaborate on how the real-time decentralised fine-tuning differentiates hyperhrt from other language models in practical scenarios?