The embedding layer in LLM is a critical component that maps discrete input tokens (words, subwords, or characters) into continuous […]
From Tokens To Vectors: Demystifying LLM Embedding For Contextual Understanding Read More »
Transformers have revolutionized the field of NLP. Central to their success is the attention mechanism, which has significantly improved how
Attention Mechanism: The Heart of Transformers Read More »
Neural networks have revolutionized various fields, from image and speech recognition to natural language processing. The primary goal of training
Optimization Techniques in Neural Networks: A Comprehensive Guide Read More »
The loss function quantifies the difference between the predicted output by the model and the actual output (or label) in
An In-Depth Exploration of Loss Functions Read More »
Neural networks are inspired by the human brain, where neurons communicate through synapses. Just as biological neurons are activated when
Activation Functions: The Key to Powerful Neural Networks Read More »
In the rapidly evolving landscape of artificial intelligence, the demand for efficient, accurate, and resource-friendly language models has never been
Squid: A Breakthrough On-Device Language Model Read More »
To compute the number of tokens in a Vision Transformer (ViT), it’s essential to understand how images are processed and
Token Tally: Quantifying Vision Transformer Tokens Read More »
Low-Rank Adaptation (LoRA) is a novel and efficient method for fine-tuning large language models (LLMs). By leveraging low-rank matrix decomposition,
Understanding LoRA Technology for LLM Fine-tuning Read More »
What is Reinforcement Learning (RL)? Imagine you’re playing a video game, and every time you achieve a goal—like defeating a
Reinforcement Learning: A Beginner’s Guide Read More »
