Smarter data generation for faster Speculator training

This blog post discusses techniques for optimizing the training of speculator models in large language models (LLMs) to improve throughput and reduce latency during inference. It introduces speculative decoding, which involves using a smaller 'speculator' model to propose multiple tokens in parallel for validation by a larger 'verifier' model. Key findings include effective data strategies such as cross-distillation among model families and optimal training epochs to maximize performance. The use of the Speculators library is highlighted, providing insights into the efficiency and quality trade-offs in training LLMs.