Summary of Tqcompressor: Improving Tensor Decomposition Methods in Neural Networks Via Permutations, by V. Abronin et al.
TQCompressor: improving tensor decomposition methods in neural networks via permutations
by V. Abronin, A. Naumov, D. Mazur, D. Bystrov, K. Tsarova, Ar. Melnikov, I. Oseledets, S. Dolgov, R. Brasher, M. Perelshtein
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 Medium Difficulty summary: The paper introduces TQCompressor, a novel method for compressing neural network models. It addresses the computational and storage demands of pre-trained language models in NLP tasks by proposing a permutation-based enhancement to Kronecker decomposition. This enhancement reduces loss in model expressivity associated with factorization. The authors demonstrate this method on GPT-2, resulting in TQCompressedGPT-2 with 81 million parameters compared to 124 million in the original GPT-2 small. They also make TQCompressedGPT-2 publicly available and enhance its performance through multi-step knowledge distillation using only 3.1% of the OpenWebText dataset. The compressed model surpasses DistilGPT-2 and KnGPT-2 in comparative evaluations, making it a significant advancement for efficient model deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about a new way to make neural networks smaller without losing their ability to learn. Right now, language models are very big and use up lots of computer power and storage space. The authors came up with a new method called TQCompressor that makes these models more efficient. They tested it on a specific model called GPT-2 and were able to reduce its size while keeping its ability to learn intact. This is important because it means we can use language models on devices that don’t have as much power or storage space. |
Keywords
* Artificial intelligence * Gpt * Knowledge distillation * Neural network * Nlp
