long-context

The long-context skill focuses on extending the effective context window of transformer-based language models beyond their original limits. It addresses the challenge of processing long documents—ranging from 32k to 128k+ tokens—by applying advanced positional encoding and attention bias techniques. Standard transformer models are often constrained by fixed context sizes defined during pre-training. This skill enables practitioners to overcome those constraints using methods such as Rotary Position Embeddings (RoPE), YaRN, ALiBi (Attention with Linear Biases), and position interpolation strategies, allowing models to generalize to longer sequences with improved stability and efficiency.

Core capabilities include implementing and adapting rotary embeddings for query and key transformations, generating ALiBi attention slopes for head-specific biasing, and applying interpolation or extrapolation strategies to extend positional ranges. The skill supports both training and fine-tuning workflows, making it suitable for length extrapolation research as well as production deployment. It integrates with widely used libraries such as transformers and torch, and can leverage flash-attn for performance optimization. Example implementations demonstrate how to construct rotary embedding modules, apply positional rotations within attention layers, and configure attention biases for scalable long-sequence modeling.

This skill is designed for machine learning engineers, researchers, and infrastructure teams working with large language models such as LLaMA or Mistral. It is particularly valuable in scenarios involving long-form document processing, retrieval-augmented systems, extended-context chat applications, and domain-specific fine-tuning where longer input sequences are essential. Teams seeking efficient context extension without retraining models from scratch can use these techniques to enhance model capacity while maintaining computational feasibility.