In our previous blog, we talked about the theory related to transfer learning. We discussed the recommended method for transfer learning: Freeze the layers with weights from the pretrained model for the first
Recently, I took the opportunity to co-maintain an open-source repository called Needle in a Haystack (NIAH). NIAH is a benchmarking technique for Large Language Models (LLMs), where the model is tasked with identifying
At True Sparrow, we enjoy delving into the user persona, understanding their challenges, and brainstorming solutions to address them. Once a consensus is reached, we often provide low-fidelity demos to clients to gather
You would think transfer learning is very straightforward and simple. Alas, why not, you just initialize weights from a proven NN which solved a different, but similar enough problem. Simple enough. Right? Yes,
In a fast-paced development environment, we often encounter various types of blocking dependencies within or between teams. One such dependency is getting the backend APIs ready to be integrated into consumer interfaces and
Allowing users to upload files on consumer applications is a common requirement. Services allow images, videos, CSV, and other file formats to be uploaded for further processing and usage. While designing the file
OpenAI and similar services have democratized access to AI, making it more accessible to everyone without worrying too much about the technical complexities of hosting and serving. On the other hand, newer and
In today’s world, vast amounts of data are being generated and consumed online & having smart search functionality is a must. Traditional search techniques are keyword-based solutions and have limitations. Specialized search
LLMs (Large Language Models) have revolutionized the technology world over the past few years. All these LLMs are available in different configurations, some need high-end GPU instances while others can be used on
We worked on Thursday with a team of 11 developers working remotely. Throughout this time, we were building at least 4 to 5 features simultaneously. For UX and QA reviews, we only had
