Speculative decoding speeds up autoregressive text generation by combining a small draft model with a larger verifier model. This two-step dance slashes latency while preserving quality, an essenti...

Mixture of Experts (MoE) lets you scale transformer models to billions of parameters without proportional compute costs. By selectively routing tokens through specialized experts, MoE achieves mass...

Flash Attention played a major role in making LLMs more accessible to consumers. This algorithm embodies how a set of what one might consider "trivial ideas" can come together and form a powerful s...

In this post, we explore how to simplify and optimize the output generation process in language models using guidance techniques. By pre-structuring inputs and restraining the output space, we can ...

The amount of visual data that we constantly ingest is massive, and our ability to function in an environment may greatly impove when we have access to this modality, thus being able to use it as a...

You've probably heard of the Transformers by now, they're everywhere, so much so that new born babies are gonna start saying Transformers as their first word, this blog will explore an important co...

If you're familiar with the Attention Mechansim, then you know that before applying a softmax to the attention scores, we need to rescale them by a factor of $\frac{1}{\sqrt{D_k}}$ where $D_k$ is t...

In this blog we will shed the light into a crucial component of the Transformers architecture that hasn't been given the attention it deserves, and you'll also get to see some pretty vizualizations!

In this post we will explore how exploding and vanishing gradients may happen, and how normalization and a change of activation functions can help us deal with these issues.

Let's kick start your journey into reinforcement learning with a cool taxi-driving simulation! You'll get hands-on with Q-learning, starting from random exploration all the way to nailing it. Plus,...