LinearARD: Linear-Memory Attention Distillation for RoPE Restoration

1. 1Identify the need to extend context windows in Large Language Models
2. 2Apply Rotary Position Embeddings (RoPE) scaling and Continual Pre-Training (CPT)
3. 3Use LinearARD self-distillation to restore original model capabilities
4. 4Evaluate the restored model on standard short-text benchmarks

ML researchers and engineers working on Large Language Models can benefit from LinearARD to improve model performance on short-text benchmarks without sacrificing long sequence processing capabilities

💡 LinearARD can restore original model capabilities disrupted by RoPE scaling and CPT

💡 LinearARD: a self-distillation method to restore RoPE in Large Language Models