Introduction In this post, we dive into a new release by Meta AI, presented in a research paper titled Sapiens: Foundation for Human Vision Models, which presents a family of models that target four fundamental human-centric tasks, which we see in the demo above. Fundamental Human-centric Tasks In the above figure from the paper, we […]
Sapiens: Foundation for Human Vision Models Read More »
Motivation In recent years, we use AI for more and more use cases, interacting with models that provide us with remarkable outputs. As we move forward, the models we use are getting larger and larger, and so, an important research domain is to improve the efficiency of using and training AI models. Standard MoE Is
Mixture of Nested Experts: Adaptive Processing of Visual Tokens Read More »
In recent years, large language models are in charge of remarkable advances in AI, with models such as GPT-3 and 4 which are closed source and with open-source models such as LLaMA 2 and 3, and many more. However, as we moved forward, these models got larger and larger and it became important to find
Introduction to Mixture-of-Experts (MoE) Read More »
Motivation In recent years we witness remarkable advancements in AI and specifically in natural language understanding, which are driven by large language models. Today, there are various different LLMs out there such as GPT-4, Llama 3, Qwen, Mixtral and many more. In this post we review a recent paper, titled: “Mixture-of-Agents Enhances Large Language Model
Mixture-of-Agents Enhances Large Language Model Capabilities Read More »
Introduction In the recent years, we witness remarkable success driven by large language models (LLMs). While LLMs perform well in various domains, such as natural language problems and code generation, there is still a lot of room for improvement with complex multi-step and algorithmic reasoning. To do research about algorithmic reasoning capabilities without pouring significant
Arithmetic Transformers with Abacus Positional Embeddings Read More »
In this post we dive into Consistency Large Language Models, or CLLMs in short, which were introduced in a recent research paper that goes by the same name. Before diving in, if you prefer a video format then check out the following video: Motivation Top LLMs such as GPT-4, LLaMA3 and more, are pushing the
CLLMs: Consistency Large Language Models Read More »
In this post we dive into a recent research paper which presents a promising novel direction for fine-tuning LLMs, achieving remarkable results when considering both parameters count and performance. Before diving in, if you prefer a video format then check out the following video: Motivation – Finetuning a Pre-trained Transformer is Expensive A common method
ReFT: Representation Finetuning for Language Models Read More »
Many of the top large language models today such as GPT-4, Claude 3 and Gemini are closed source, so a lot about the inner workings of these models is not known to the public. One justification for this is usually the competitive landscape, since companies are investing a lot of money and effort to create
Stealing Part of a Production Language Model Read More »
In this post, we dive into V-JEPA, which stands for Video Joint-Embedding Predicting Architecture, a new collection of vision models by Meta AI. V-JEPA is another step in Meta AI’s implementation of Yann LeCun’s vision about a more human-like AI. Several months back, we’ve already covered Meta AI’s I-JEPA model, which is a JEPA model
How Meta AI ‘s Human-Like V-JEPA Works? Read More »
In this post, we dive into a new and exciting research paper by Microsoft, titled: “The Era of 1-bit LLMs: All Large Language Models are 1.58 bits”. In recent years, we’ve seen a tremendous success of large language models with models such as GPT, LLaMA and more. As we move forward, we see that the
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits Read More »
