Summary of Compactifai: Extreme Compression Of Large Language Models Using Quantum-inspired Tensor Networks, by Andrei Tomut et al.
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks
by Andrei Tomut, Saeed S. Jahromi, Abhijoy Sarkar, Uygar Kurt, Sukhbinder Singh, Faysal Ishtiaq, Cesar Muñoz, Prabdeep Singh Bajaj, Ali Elborady, Gianni del Bimbo, Mehrazin Alizadeh, David Montero, Pablo Martin-Ramiro, Muhammad Ibrahim, Oussama Tahiri Alaoui, John Malcolm, Samuel Mugel, Roman Orus
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Quantum Physics (quant-ph)
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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: This research paper introduces CompactifAI, a novel Large Language Model (LLM) compression approach inspired by quantum mechanics. The method focuses on the model’s correlation space instead of reducing the number of neurons or precision of weights. This innovative technique allows for a more controlled and interpretable model compression. The authors demonstrate that combining CompactifAI with quantization can significantly reduce the memory size, number of parameters, training time, and inference time of an LLM while maintaining accuracy. This breakthrough has implications for the overparametrization of standard LLMs, suggesting that they may not need to be as large as currently thought. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine a super smart computer program called a Large Language Model (LLM) that can generate text like a human. But these models are huge and take up a lot of space and energy. Scientists have been trying to make them smaller without losing their ability to understand language. This paper presents a new way to shrink these models while keeping them intelligent. The method uses ideas from quantum mechanics to better compress the information in the model. The results show that this approach can significantly reduce the size and processing time of the LLM, making it more practical for use. |
Keywords
* Artificial intelligence * Inference * Large language model * Model compression * Precision * Quantization
