🎯 RoPE Embeddings with YaRN

Interactive Visualization of Rotary Position Embeddings

Yet another RoPE extensioN (YaRN) Scaling Analysis

🎛️ Configuration Parameters

YaRN Scaling Factor: 4.0×

Head Dimension: 32 64 128

Sequence Length: 2048

Base Frequency:

Initial Context Length:

8192

Effective Context

32

Frequency Pairs

65536

Max Wavelength

2

Min Wavelength

📊 Frequency Analysis

📈 Wavelength Comparison

📉 Frequency Scaling Ratio

🎯 YaRN Ramp Function Effect

🔥 RoPE Embeddings Heatmap

ℹ️ About YaRN and RoPE

Rotary Position Embedding (RoPE) encodes positional information by rotating query and key vectors using rotation matrices based on position and dimension.

YaRN (Yet another RoPE extensioN) extends the effective context length by intelligently scaling frequency components, allowing models to handle longer sequences than their training context.

Key Benefits: YaRN maintains performance on shorter sequences while enabling processing of much longer sequences, making it ideal for long-form text generation and analysis.