Summary of Espace: Dimensionality Reduction Of Activations For Model Compression, by Charbel Sakr and Brucek Khailany
ESPACE: Dimensionality Reduction of Activations for Model Compression
by Charbel Sakr, Brucek Khailany
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The proposed ESPACE method is an innovative technique for compressing Large Language Models (LLMs) by reducing dimensionality of activations. Unlike previous approaches, ESPACE focuses on projecting activations onto pre-calibrated principal components, enabling retraining with no loss of expressivity and obtaining weight decomposition as a byproduct during matrix multiplication. Theoretical results demonstrate optimal computational accuracy in constructing projection matrices. Experimentally, ESPACE achieves 50% compression for GPT3, Llama2, and Nemotron4 models with minimal accuracy degradation, while also reducing execution time and inference latency on existing hardware. Compared to matrix factorization-based approaches for compressing Llama2-7B, ESPACE advances the state-of-the-art in tensor decomposition compression of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ESPACE is a new way to make Large Language Models smaller without losing their abilities. This method works by changing how activations are represented, not just focusing on the model’s weights like other methods do. By doing this, ESPACE allows models to be retrained with no loss in performance, and it even helps during inference (when the model is used to generate text). In experiments, ESPACE was able to shrink GPT3, Llama2, and Nemotron4 models by 50% while only slightly reducing their accuracy. This technique also reduces the time it takes for computers to perform calculations and prepares the model for use. |
Keywords
» Artificial intelligence » Inference
