Aayush Mittal

Diffusion models have emerged as a powerful approach in generative AI, producing state-of-the-art results in image, audio, and video generation. In this in-depth technical article, we'll explore how diffusion models work, their key innovations, and why they've become so successful. We'll cover the mathematical foundations, training process, sampling algorithms, and cutting-edge applications of this exciting new technology.

As developers and dta scientists, we often find ourselves needing to interact with these powerful models through APIs. However, as our applications grow in complexity and scale, the need for efficient and performant API interactions becomes crucial. This is where asynchronous programming shines, allowing us to maximize throughput and minimize latency when working with LLM APIs.

Computational power has become a critical factor in pushing the boundaries of what's possible in machine learning. As models grow more complex and datasets expand exponentially, traditional CPU-based computing often falls short of meeting the demands of modern machine learning tasks. This is where CUDA (Compute Unified Device Architecture) comes, an approach to accelerate machine learning workflows.

Reflection 70B is an open-source large language model (LLM) developed by HyperWrite. This new model introduces an approach to AI cognition that could reshape how we interact with and rely on AI systems in numerous fields, from language processing to advanced problem-solving. Leveraging Reflection-Tuning, a groundbreaking technique that allows the model to self-assess and correct...

As the demand for large language models (LLMs) continues to rise, ensuring fast, efficient, and scalable inference has become more crucial than ever.

In the constantly evolving field of molecular biology, one of the most challenging tasks has been designing proteins that can effectively bind to specific targets, such as viral proteins, cancer markers, or immune system components. These protein binders are crucial tools in drug discovery, disease treatment, diagnostics, and biotechnology. Traditional methods of creating these protein binders are labor-intensive, time-consuming, and often require numerous rounds of optimization. However, recent advances in artificial intelligence (AI) are dramatically accelerating this process.

The race to dominate the enterprise AI space is accelerating with some major news recently.

On October 17, 2024, Microsoft announced BitNet.cpp, an inference framework designed to run 1-bit quantized Large Language Models (LLMs). BitNet.cpp is a significant progress in Gen AI, enabling the deployment of 1-bit LLMs efficiently on standard CPUs, without requiring expensive GPUs. This development democratizes access to LLMs, making them available on a wide range of...

Microsoft Research introduced AutoGen in September 2023 as an open-source Python framework for building AI agents capable of complex, multi-agent collaboration. AutoGen has already gained traction among researchers, developers, and organizations, with over 290 contributors on GitHub and nearly 900,000 downloads as of May 2024. Building on this success, Microsoft unveiled AutoGen Studio, a low-code...

The LLM-as-a-Judge framework is a scalable, automated alternative to human evaluations, which are often costly, slow, and limited by the volume of responses they can feasibly assess. By using an LLM to assess the outputs of another LLM, teams can efficiently track accuracy, relevance, tone, and adherence to specific guidelines in a consistent and replicable manner.