AI Practitioner

Implementation of RoPE Rotary Position Embedding (RoPE) is a positional encoding method introduced in the 2021 RoFormer paper ( https://arxiv.org/pdf/2104.09864 ). This technique overcomes the limitations of absolute positional encoding and enhances a Transformer model's ability to capture sequence order and relative positions effectively. 1. Limitations of Traditional Positional Encoding Since Transformers cannot inherently model token order, positional encodings are added to token embeddings. Early models used sinusoidal encodings, and later learnable embeddings were introduced. However, these approaches have several drawbacks: They encode absolute rather than relative positions, reducing contextual precision Struggle with generalizing to sequences of varying lengths Increase model parameters and often degrade in long-range dependencies ...

What is a Transformer? The Transformer is a neural network architecture introduced by Vaswani et al. in the 2017 paper "Attention is All You Need." It revolutionized natural language processing by replacing sequential models like RNNs and LSTMs. Transformers process entire sentences in parallel using self-attention , effectively addressing the difficulty of learning from long input sequences and enabling high computational efficiency by overcoming the limitations of sequential processing. 1. Transformer Components and Overcoming RNN/LSTM Limitations The Transformer is composed of an encoder and a decoder, with each block consisting of the following key components: Self-Attention: Learns the relationships between tokens within the input sequence by enabling each token to attend to all others, effectively capturing long-range dependencies and rich contextual information. Multi-Head Attention (MHA): Divides self-attention into multiple parallel heads. Each head focuses o...