Tag: transformer architecture
Sinusoidal vs Learned Positional Encoding: Why Modern LLMs Use RoPE and ALiBi
Compare sinusoidal vs learned positional encoding in Transformers. Discover why modern LLMs like Llama 3 use RoPE and ALiBi for better long-context performance and extrapolation.
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How to Plan Memory for LLM Inference and Avoid OOM Errors
Learn how to plan memory for LLM inference to avoid OOM errors. Explore techniques like CAMELoT, Larimar, and Dynamic Memory Sparsification to optimize performance.
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Mastering Positional Encoding in Transformer Generative AI Models
Explore how positional encoding gives order to Transformer models, covering sinusoidal methods, learned embeddings, and modern techniques like RoPE for better generative AI.
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Contextual Representations in Large Language Models: What LLMs Understand about Meaning
Discover how modern AI understands meaning through context. Learn about context windows, attention mechanisms, and why LLMs interpret words differently based on surrounding text.
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Rotary Position Embeddings and ALiBi: How Modern LLMs Handle Position Without Learned Embeddings
Rotary Position Embeddings and ALiBi are the two leading methods modern LLMs use to handle sequence position without learned embeddings. They enable longer context, better extrapolation, and faster training-replacing old positional encoding techniques entirely.
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From Markov Models to Transformers: A Technical History of Generative AI
This article traces the technical evolution of generative AI from early probabilistic models like Markov chains to modern transformer architectures. Learn how breakthroughs in neural networks, GANs, and attention mechanisms shaped today's AI capabilities-and the challenges still ahead.
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Large Language Models: Core Mechanisms and Capabilities Explained
Large language models power today’s AI assistants by using transformer architecture and attention mechanisms to process text. Learn how they work, what they can and can’t do, and why size isn’t everything.
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