Summary of Training-free Activation Sparsity in Large Language Models, by James Liu et al.
Training-Free Activation Sparsity in Large Language Models
by James Liu, Pragaash Ponnusamy, Tianle Cai, Han Guo, Yoon Kim, Ben Athiwaratkun
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a novel method called TEAL (Training-Free Efficient Activation-based Learning) to achieve practical inference speedups in large language models (LLMs). By applying magnitude-based activation sparsity to hidden states throughout the entire model, TEAL achieves 40-50% model-wide sparsity with minimal performance degradation. The method is compatible with weight quantization, enabling further efficiency gains. The paper demonstrates wall-clock decoding speed-ups of up to 1.53and 1.8at 40% and 50% model-wide sparsity on various LLM families, including Llama-2, Llama-3, and Mistral. TEAL’s simplicity and training-free nature make it a promising approach for practical inference speedups in large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make big language models work faster without needing to retrain them from scratch. The model is called TEAL, which makes the hidden parts of the language model smaller without losing its ability to understand text. This means the computer can do more work with less power and memory. The researchers tested this idea on different types of large language models and found that it works well, making the decoding process faster by up to 1.8 times. |
Keywords
» Artificial intelligence » Inference » Language model » Llama » Quantization
