How GPUs are used to train Large Language Models LLMs and why they are so effective. How does TFLOPs

Deep dive into the engineering of scaling a production ready AI-powered sales monitoring system. Leveraging a complex pipeline of LLMs, ASR, and diarization to monitor calls of more than 5000+ salespeople

A critique of the current AI narrative and its limitations, and a call for a more comprehensive approach to understanding and replicating human intelligence.

An in-depth introduction to Vision Transformers and their potential to revolutionize the field of computer vision.

Deep-dive into Paligemma Google's innovative vision language model. Learn about its architecture, capabilities, and potential applications in AI and machine learning.

How SigLIP works and addresses the shortcomings of contrastive loss of LIP Language Image Pretraining

Understand the Transformer model and its architecture for speech recognition. Learn how Transformers are used in ASR systems and how they compare to traditional RNNs and LSTMs

Phi 3, developed by Microsoft AI, is a highly capable language model that can run on a phone

LLaVA is a multimodal large language model that has a vision encoder and a language encoder. It is trained on a large-scale multimodal dataset and can generate text conditioned on images

Llama 3 by Meta AI is trained on more than 15T tokens of data. It is currently the SOTA open-weights LLM available beating out the competition by a fair margin. It will be released in three different variants: 8B, 70B and 400B.

Grouped Query Attention is a new attention mechanism that can be used to improve the performance of transformer models.

An in-depth look at RoPE, a novel positional encoding method that uses sinusoidal embeddings to capture the relative positions of tokens in a sequence.

Understand the Multi Query Attention mechanism in the context of Transformers and how it helps in capturing multiple aspects of the input sequence

Gemma is a state of the art family of models developed by Google Research. Gemma models are open-source and can be used for a variety of tasks.

A deep dive into the architecture and inner workings of the revolutionary generative model, Stable Diffusion developed by Stability AI

An in-depth and practical guide into how generative AI models are bridging the gap between vision and language using Contrastive Learning approaches as seen in CLIP, released by OpenAI in 2021.

An in-depth guide into the architecture of Mistral AI's state of the art Sparse Mixture of Experts (SMoE) model, Mixtral of Experts

Mixture of Experts (MoE) architecture, a cutting-edge approach revolutionizing Large Language Models (LLMs). Let's explore how MoE enhances LLM performance by leveraging specialized expert networks, leading to more efficient and accurate language processing

RAG has become the standard architecture for providing LLMs with the relevant context to reduce hallucinations and improve performance. In this blog post, we will look at the RAG triad and how it can be used to evaluate quality of response from LLMs

Deep dive into understanding the basic building blocks of Denoising Diffusion Probabilistic Models and code implementation using PyTorch.

An in-depth look at GPT-2, a large language model developed by OpenAI, and its architecture, training, and applications.

Understand how the widely used BERT model works and its architecture is related to the Transformer model. Also, learn how BERT is pre-trained and fine-tuned for various NLP tasks

Transformers have become the building blocks of many state-of-the-art AI models. This post provides a detailed explanation of the Transformer architecture which was introduced in the paper 'Attention is All You Need'

A detailed guide into how tokenization strategies work in large language models. This post will cover the basics of tokenization, the different tokenization strategies, and how they are implemented in large language models

Gentle introduction to Variational Autoencoder (VAE) - a type of generative model used to learn efficient representations of data by learning the parameters of a probability distribution representing the data. We explore the architecture, training process, and the reparameterization trick used in VAEs