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A Guide to Mastering Large Language Models

Unite.AI

Large language models (LLMs) have exploded in popularity over the last few years, revolutionizing natural language processing and AI. Techniques like Word2Vec and BERT create embedding models which can be reused. Google's MUM model uses VATT transformer to produce entity-aware BERT embeddings.

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Amazon EC2 DL2q instance for cost-efficient, high-performance AI inference is now generally available

AWS Machine Learning Blog

With eight Qualcomm AI 100 Standard accelerators and 128 GiB of total accelerator memory, customers can also use DL2q instances to run popular generative AI applications, such as content generation, text summarization, and virtual assistants, as well as classic AI applications for natural language processing and computer vision.

BERT 102
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Evaluation Derangement Syndrome (EDS) in the GPU-poor’s GenAI. Part 1: the case for Evaluation-Driven Development

deepsense.ai

Within Natural Language Processing (NLP), ‘pseudo-evaluation’ approaches that we call ‘Superficial Utility Comparison Kriterion’ ( SUCK ) methods, like BLEU [32], METEOR [33], ROUGE [34], or BLEURT [35], attempt to salvage the situation. References A survey of Generative AI Applications , Gozalo-Brizuela R., Hertzmann A.,