How to Measure the Performance of LLM?
Measuring the performance of a Large Language Model (LLM) involves evaluating various aspects of its functionality, ranging from linguistic capabilities […]
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LLMs & GenAI
How To Control The Output Of LLM?
Controlling the output of a Large Language Model (LLM) is essential for ensuring that the generated content meets specific requirements,
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Byte Pair Encoding (BPE) Explained: How It Fuels Powerful LLMs
Traditional tokenization techniques face limitations with vocabularies, particularly with respect to unknown words, out-of-vocabulary (OOV) tokens, and the sparsity of
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How do LLMs Handle Out-of-vocabulary (OOV) Words?
LLMs handle out-of-vocabulary (OOV) words or tokens by leveraging their tokenization process, which ensures that even unfamiliar or rare inputs
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Quantifying Prompt Quality: Evaluating The Effectiveness Of A Prompt
Evaluating the effectiveness of a prompt is crucial to harnessing the full potential of Large Language Models (LLMs). An effective
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Pushing the Boundaries of LLM Efficiency: Algorithmic Advancements
This article summarizes the content of the source, “The Efficiency Spectrum of Large Language Models: An Algorithmic Survey,” focusing on
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Dissecting the Vision Transformer (ViT): Architecture and Key Concepts
Vision Transformers (ViT) have emerged as a groundbreaking architecture that has revolutionized how computers perceive and understand visual data. Introduced
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Weight Tying In Transformers: Learning With Shared Weights
Consider a bilingual dictionary. To understand a foreign word, you look it up and find its meaning. To express yourself
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Architecture of the Embedding Layer During Training of LLMs
The embedding layer in an LLM is a critical component that maps discrete input tokens (words, subwords, or characters) into
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Attention Mechanism: The Heart of Transformers
Imagine a study group where every student is allowed to look around the room before answering a question. One student
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Understanding LoRA Technology for LLM Fine-tuning
Low-Rank Adaptation (LoRA) is a novel and efficient method for fine-tuning large language models (LLMs). By leveraging low-rank matrix decomposition,
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