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Transformer? Diffusion? Transfusion!

A gentle introduction to the latest multi-modal transfusion model Recently, Meta and Waymo released their latest paper — Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model, which integrates the popular transformer model with the diffusion model for multi-modal training and prediction purposes. Like Meta’s previous work, the Transfusion model is based on the…

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Training AI Models on CPU. Revisiting CPU for ML in an Era of GPU… | by Chaim Rand | Sep, 2024

Revisiting CPU for ML in an Era of GPU Scarcity Photo by Quino Al on UnsplashThe recent successes in AI are often attributed to the emergence and evolutions of the GPU. The GPU’s architecture, which typically includes thousands of multi-processors, high-speed memory, dedicated tensor cores, and more, is particularly well-suited to meet the intensive demands…

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Tackle Complex LLM Decision-Making with Language Agent Tree Search (LATS) & GPT-4o | by Ozgur Guler | Aug, 2024

Enhancing LLM decision-making: integrating language agent tree search with GPT-4o for superior problem-solving Image by the author: midjourney — abstract puzzleLarge Language Models (LLMs) have demonstrated exceptional abilities in performing natural language tasks that involve complex reasoning. As a result, these models have evolved to function as agents capable of planning, strategising, and solving complex…

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The Evolution of SQL. Unlocking the power of large language… | by 💡Mike Shakhomirov | Aug, 2024

Unlocking the power of large language models Photo by ZHENYU LUO on UnsplashIn this article, I will examine how large language models (LLMs) can convert natural language into SQL, making query writing more accessible to non-technical users. The discussion will include practical examples that showcase the ease of developing LLM-based solutions. We’ll also cover various…

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LLM Personalization. User Persona based Personalization of… | by Debmalya Biswas | Aug, 2024

User Persona based Personalization of LLM generated Responses ChatGPT, or the underlying large language models (LLMs) today, are able to generate contextualized responses given a prompt. As a next step in the LLM evolution, we expect the responses to be more and more personalized with respect to the persona, conversation history, current conversation context and…

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