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.
8.7
Rating
0
Installs
AI & LLM
Category
Exceptional skill for extending transformer context windows. The description clearly specifies use cases (32k-128k+ tokens, extending pre-trained models) and techniques (RoPE, YaRN, ALiBi). Task knowledge is comprehensive with working code examples for all major techniques, comparison tables, fine-tuning guides, and production deployment patterns. Structure is excellent with logical flow from quick start to advanced patterns, though slightly dense in the main file. High novelty - implementing long-context extensions requires deep understanding of positional encodings, specialized fine-tuning strategies, and optimization techniques that would consume many tokens for a CLI agent to discover independently. The skill effectively packages complex research (4 major papers) into actionable implementations with clear trade-offs and best practices.
davila7
Skill Author
Loading SKILL.md…
Skill Author
