Individual skill
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.
backend/cli/skills/llm-tools/long-context/SKILL.md
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- Last checked
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- Nov 7, 2026
- License
- Apache-2.0
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- agent Repository topic
- ai Repository topic
- bun Repository topic
- cli Repository topic
- llm Repository topic
- ml Repository topic
- open-source Repository topic
- research Repository topic
- science Repository topic
- ai-agent Repository topic
- emerging-techniques Declared skill tag
- long-context Declared skill tag
- rope Declared skill tag
- yarn Declared skill tag
- alibi Declared skill tag
- position-interpolation Declared skill tag
- extended-context Declared skill tag
- rotary-embeddings Declared skill tag
- attention-bias Declared skill tag
- context-extension Declared skill tag
- positional-encoding Declared skill tag
- engineering Derived category
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Read excerpt
# 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
