Enhancing Efficiency of Large Language Models (LLMs) with Q-Sparse
Practical Solutions and Value
Recent research aims to enhance Large Language Model (LLM) efficiency through quantization, pruning, distillation, and improved decoding. Q-Sparse enables full activation sparsity, significantly enhancing inference efficiency, achieving baseline LLM performance with lower inference costs, and offering a path to more efficient, cost-effective, and energy-saving LLMs.
Q-Sparse enhances the Transformer architecture by enabling full sparsity in activations through top-K sparsification and the straight-through estimator (STE). It supports full-precision and quantized models, including 1-bit models. The approach applies a top-K function to the activations during matrix multiplication, reducing computational costs and memory footprint.
Studies show that LLM performance scales with model size and training data follow a power law. Q-Sparse complements MoE and will be adapted for batch processing to enhance its practicality. It is effective across various settings and compatible with full-precision and 1-bit models, making it a pivotal approach for improving LLM efficiency and sustainability.
Q-Sparse is effective for training from scratch, continue-training, and fine-tuning, maintaining efficiency and performance across various settings. The performance gap between sparse and dense models diminishes with increasing model size, indicating that sparse models can efficiently match or outperform dense models with proper sparsity.
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