What problem does it solve? Pre-trained transformer models like LLaMA and Mistral are limited to their original context windows (typically 2k-8k tokens), making them unable to process long documents, codebases, or conversations without expensive retraining from scratch. ## Core Features & Use Cases - Positional Encoding Implementations: Provides complete PyTorch implementations of RoPE (Rotary Position Embeddings), ALiBi (Attention with Linear Biases), and YaRN scaling for extending context to 32k-128k+ tokens. - Fine-tuning Pipelines: Includes minimal fine-tuning configurations (400-1000 steps) for position interpolation and YaRN-based context extension with HuggingFace Transformers. - Evaluation & Deployment: Covers perplexity evaluation, passkey retrieval testing, memory optimization with Flash Attention 2, and vLLM deployment for long-context inference. - Use Case: Extend a LLaMA-2-7B model from 2k to 32k tokens by configuring YaRN rope_scaling, fine-tuning for 400 steps on long documents like PG-19, and deploying with vLLM for long-document question answering. ## Quick Start Extend my LLaMA-2-7B model to handle 32k token contexts using YaRN scaling and show me the fine-tuning configuration.
