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Supercharge Your LLM Apps Using DSPy and Langfuse | by Raghav Bali | Oct, 2024

As illustrated in figure 1, DSPy is a pytorch-like/lego-like framework for building LLM-based apps. Out of the box, it comes with: Signatures: These are specifications to define input and output behaviour of a DSPy program. These can be defined using short-hand notation (like “question -> answer” where the framework automatically understands question is the input…

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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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