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long-context

Extend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ tokens), extending pre-trained models beyond original context limits, or implementing efficient positional encodings. Covers rotary embeddings, attention biases, interpolation methods, and extrapolation strategies for LLMs.

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# 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