long-context

You can extend transformer context windows to 32k+ tokens by applying techniques like RoPE, YaRN, ALiBi, and Position Interpolation. These methods require configuring rope_scaling or per-method settings, followed by lightweight fine-tuning to validate long-document performance.

RoPE position interpolation rescales position frequencies to fit longer sequences, while YaRN combines NTK-aware interpolation with scaling for improved extrapolation. Both are supported for extending context windows, and they can be integrated together with ALiBi biases for enhanced long-context handling.

Yes, lightweight fine-tuning is required after configuring rope_scaling or ALiBi settings to validate long-context performance. You prepare long-context datasets like PG-19 and run minimal training passes to ensure the model adapts to the extended 32k+ token range without retraining from scratch.

Yes, you can combine ALiBi biases with YaRN NTK-aware interpolation within a single transformer model. This Skill provides guidance on integrating multiple long-context techniques simultaneously, allowing you to leverage both attention bias and position scaling for effective context extension.

Position interpolation extends context windows by rescaling frequencies but may require careful tuning and lightweight fine-tuning to maintain performance. Without validating on long-context datasets like PG-19, the model may exhibit degraded reasoning or attention quality at extended sequence lengths beyond 32k tokens.