Summary of Q-sparse: All Large Language Models Can Be Fully Sparsely-activated, by Hongyu Wang et al.
Q-Sparse: All Large Language Models can be Fully Sparsely-Activated
by Hongyu Wang, Shuming Ma, Ruiping Wang, Furu Wei
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Q-Sparse, a novel approach for training large language models (LLMs) with sparse activations. The method employs top-K sparsification and straight-through-estimator to achieve full sparsity in LLMs, leading to significant efficiency gains during inference. The authors also propose Block Q-Sparse for batch training and inference. The key findings include: comparable results to baseline LLMs at a fraction of the computational cost; an inference-optimal scaling law for sparsely-activated LLMs; effectiveness across various settings, including training-from-scratch, continued training, and fine-tuning; and applicability to both full-precision and 1-bit LLMs. Q-Sparse is particularly noteworthy when combined with MoE (Multilingual Embeddings) and BitNet b1.58, offering a path towards revolutionizing the efficiency of future LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way to make large language models more efficient. The model, called Q-Sparse, uses a clever trick to reduce the amount of computation needed during inference. This can lead to big savings in time and energy. The authors also show that their method works well across different scenarios and even with smaller, 1-bit versions of these models. The combination of Q-Sparse and another technique called MoE (Multilingual Embeddings) could help create more efficient language models in the future. |
Keywords
* Artificial intelligence * Fine tuning * Inference * Precision
