emerging-techniques-long-context

原文涵盖的主题

• Long Context: Extending Transformer Context Windows
• When to Use This Skill
• Installation
• Quick Start
• RoPE (Rotary Position Embeddings)
• ALiBi (Attention with Linear Biases)
• Position Interpolation for LLaMA
• Core Concepts

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) How it works: Encodes absolute position via rotation matrix Provides relative position dependency in attention Enables length extrapolation Mathematical formulation: Advantages: Decaying inter-token dependency with distance Compatible with linear attention Better extrapolation than absolute position encodings Key innovation: NTK-aware interpolation (Neural Tangent Kernel) Attention temperature scaling Efficient context extension (10× less tokens vs baselines) Parameters: Performance: Extends LLaMA to 128k tokens 2.5× less tra

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