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  Introduction to Mixture-of-Experts (MoE)
  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 ways to improve their efficiency. Mixture-of-Experts (MoE) to the rescue. Mixture-of-Experts (MoE) High-Level Idea One known method that has been adapted with impressive success is called Mixture-of-Experts, or MoE in short, which allows to increase models capacity without a proportional increase in computational cost. The… Read more: Introduction to Mixture-of-Experts (MoE)
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  Mixture-of-Agents Enhances Large Language Model Capabilities
  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 Capabilities”, which presents a new method, called Mixture-of-Agents, where LLMs can collaborate together as a team, and harness the collective expertise of different LLMs. So, instead of using a single LLM to get a response, we can get a response that is powered by multiple… Read more: Mixture-of-Agents Enhances Large Language Model Capabilities
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  Arithmetic Transformers with Abacus Positional Embeddings
  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 amount of money, a common approach is to focus on simple arithmetic problems, like addition, since addition of large numbers is a multi-step calculation. In this post we cover a fascinating recent research paper titled: Transformers Can Do Arithmetic with the Right Embeddings, which presents… Read more: Arithmetic Transformers with Abacus Positional Embeddings
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  CLLMs: Consistency Large Language Models
  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 limits of AI to remarkable advancements. When we feed a LLM with a prompt, it only generates a single token at a time. In order to generate the second token in the response, another pass of the LLM is needed, now both with the prompt… Read more: CLLMs: Consistency Large Language Models

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