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klotz: deep learning* + llm* + transformers*

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1. Understanding Attention in LLMs

   The attention mechanism in Large Language Models (LLMs) helps derive the meaning of a word from its context. This involves encoding words as multi-dimensional vectors, calculating query and key vectors, and using attention weights to adjust the embedding based on contextual relevance.

   2025-03-07 Tags: attention, llm, machine-learning, neural networks, nlp, transformers by klotz
2. Deep Dive into Self-Attention by Hand✍︎

   Explore the intricacies of the attention mechanism responsible for fueling the transformers.

   2025-02-04 Tags: transformers, self-attention, neural networks, llm, machine learning by klotz
3. New Trends in LLM Architecture

   Discusses the trends in Large Language Models (LLMs) architecture, including the rise of more GPU, more weights, more tokens, energy-efficient implementations, the role of LLM routers, and the need for better evaluation metrics, faster fine-tuning, and self-tuning.

   2024-06-01 Tags: llm, machine learning, deep learning, transformers, self-tuning, evaluation by klotz
4. Mastering LLM Techniques: Training

   Delving into transformer networks

   2023-11-18 Tags: nvidia, llm, training, transformers, deep learning by klotz

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