Improving the memory and computational efficiency of Large Language Models (LLMs) for handling long input sequences, including retrieval augmented questions answering, summarization, and chat tasks. It covers various techniques, such as lower precision computing, Flash Attention algorithm, positional embedding methods, and key-value caching strategies. These methods help reduce memory consumption and increase inference speeds while maintaining high accuracy levels in LLM applications. Furthermore, it highlights some advanced approaches like Multi-Query-Attention (MQA) and Grouped-Query-Attention (GQA), which further enhance computational and memory efficiency without compromising performance.
How computationally optimized prompts make language models excel, and how this all affects prompt engineering
Elbow curve and Silhouette plots both are very useful techniques for finding the optimal K for K-means clustering
