Summary of Tensor Train Low-rank Approximation (tt-lora): Democratizing Ai with Accelerated Llms, by Afia Anjum et al.
Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs
by Afia Anjum, Maksim E. Eren, Ismael Boureima, Boian Alexandrov, Manish Bhattarai
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The paper introduces a novel approach called Tensor Train Low-Rank Approximation (TT-LoRA) that extends the Low-Rank Economic Tensor-Train Adaptation (LoRETTA) method for parameter-efficient fine-tuning of Large Language Models (LLMs). The authors aim to address the limitations of previous methods, such as LoRA and Adapters, which struggle with scalability and compression. TT-LoRA eliminates traditional LoRA-based structures and achieves greater model compression without compromising downstream task performance or increasing inference latency and computational overhead. The paper establishes benchmarks through an exhaustive parameter search, demonstrating significant compression of LLMs while maintaining comparable performance to larger models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make big language models smaller so they can be used on devices with limited resources. Right now, these models are too big and need a lot of computer power, which makes it hard for them to be used in many places. To solve this problem, researchers have been trying different ways to make the models smaller without losing their ability to do tasks like answering questions or translating languages. The new method, called TT-LoRA, uses a special kind of math to shrink the models down while keeping them as good at doing tasks as before. |
Keywords
* Artificial intelligence * Fine tuning * Inference * Lora * Model compression * Parameter efficient
