Efficiency Breakthroughs in LLMs: Combining Quantization, LoRA, and Pruning for Scaled-down Inference and Pre-training

In recent years, LLMs have transitioned from research tools to practical applications, largely due to their increased scale during training. However, as most of their computational resources are consumed during inference, efficient pretraining and inference are crucial. Post-training techniques like quantization, Low-Rank Adapters (LoRA), and pruning offer ways to reduce memory usage and inference time. […]

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