long-context

What problem does it solve?

This Skill enables transformer models to process and understand significantly longer text inputs than their original pre-trained context windows allow, overcoming limitations for tasks involving extensive documents.

Core Features & Use Cases

• Context Extension: Apply techniques like RoPE, YaRN, ALiBi, and Position Interpolation to extend model context windows (e.g., from 4k to 32k, 128k, or more tokens).
• Long Document Processing: Ideal for analyzing lengthy research papers, books, codebases, or transcripts.
• Use Case: Fine-tune a LLaMA model to process entire legal contracts (30k+ tokens) for summarization or question-answering, tasks impossible with its default 4k context.

Quick Start

Apply position interpolation to extend the context window of a LLaMA model to 32k tokens.