Long Context

Long Context: Extending Transformer Context Windows

When to Use This Skill

Use Long Context techniques when you need to:

• Process long documents (32k, 64k, 128k+ tokens) with transformer models
• Extend context windows of pre-trained models (LLaMA, Mistral, etc.)
• Implement efficient positional encodings (RoPE, ALiBi)
• Train models with length extrapolation capabilities
• Deploy models that handle variable-length inputs efficiently
• Fine-tune existing models for longer contexts with minimal compute

Key Techniques: RoPE (Rotary Position Embeddings), YaRN, ALiBi (Attention with Linear Biases), Position Interpolation

Papers: RoFormer (arXiv 2104.09864), YaRN (arXiv 2309.00071), ALiBi (arXiv 2108.12409), Position Interpolation (arXiv 2306.15595)

Installation

# HuggingFace Transformers (includes RoPE, YaRN support)
pip install transformers torch

# For custom implementations
pip install einops  # Tensor operations
pip install rotary-embedding-torch  # Standalone RoPE

# Optional: FlashAttention for efficiency
pip install flash-attn --no-build-isolation

Quick Start

RoPE (Rotary Position Embeddings)