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Adam Kiani
PTA Academy
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Large Language Models (LLMs) evaluation is essential for assessing the performance, reliability, and safety of AI models. The evaluation process can be categorized into three levels: Basic, Intermediate, and Advanced. Each level incorporates different methods and benchmarks to ensure the model meets the specified requirements.
Training Large Language Models (LLMs) involves multiple stages, from initial pretraining on large datasets to advanced fine-tuning for specific applications. This document outlines the essential steps in training LLMs across different stages: Basic, Intermediate, and Advanced.
Multimodal AI refers to systems that process and integrate multiple types of data, such as text, images, audio, and video. Training and developing multimodal models require different techniques depending on the complexity and the type of modalities involved. This document outlines the different levels of multimodal AI development: Basic, Intermediate, and Advanced.
Factuality in Large Language Models (LLMs) refers to their ability to generate responses that are accurate, truthful, and aligned with verified sources. Ensuring factuality is crucial for reducing misinformation, improving reliability, and enhancing trust in AI-generated content. This document outlines different levels of factuality improvements, from basic methods to advanced techniques.
Generative AI refers to artificial intelligence models that create new content, including text, images, music, and more. It has revolutionized various industries, from creative arts to software development. This document outlines the progression of generative AI from basic principles to advanced techniques.
Artificial Intelligence (AI) and data are deeply interconnected, with AI systems relying on data for training, learning, and decision-making. This document explores the journey of AI and data from basic concepts to advanced applications.
Custom engineering involves designing, developing, and optimizing solutions tailored to specific industry needs. This document explores the journey from basic principles to advanced applications in custom engineering.
LLM alignment and safety are critical for ensuring AI-generated responses align with human values, ethical principles, and factual correctness while minimizing risks such as bias, misinformation, and harmful content. This document outlines the progression of LLM alignment and safety techniques from basic to advanced levels.
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