LLM Modules: Knowledge Transfer from a Large to a Small Model using Enhanced Cross-Attention
Konstantin Kolomeitsev
2025-02-13
Summary
This paper talks about a new way to make smaller AI language models smarter by learning from bigger ones, using a special technique called Enhanced Cross-Attention. It's like teaching a small computer to think more like a big, powerful one.
What's the problem?
Big AI language models are really smart, but they need a lot of computer power to work. Smaller models are easier to use, but they're not as clever. Scientists want to find a way to make the smaller models learn from the big ones without needing so much computer power.
What's the solution?
The researchers created something called LLM Modules. They took a big AI model (Qwen2-1.5B) and connected it to a smaller one (GPT-Neo-125M) using a special attention system. This system helps the small model learn from the big one without having to change the big model at all. They tested this on a dataset called Bespoke-Stratos-17k and found that after some training, the small model could give answers almost as good as more complicated methods.
Why it matters?
This matters because it could make advanced AI more accessible to people who don't have super powerful computers. It means we might be able to have really smart AI assistants on our phones or personal computers, not just in big tech companies. This could lead to more people being able to use and benefit from advanced AI technology in their daily lives or work.
Abstract
In this work, we propose an architecture of LLM Modules that enables the transfer of knowledge from a large pre-trained model to a smaller model using an Enhanced Cross-Attention mechanism. In the proposed scheme, the Qwen2-1.5B model is frozen and its representations are passed through specially designed attention layers to the GPT-Neo-125M model, which is trained on limited computational resources. Experimental results on the Bespoke-Stratos-17k dataset demonstrate that after 15 epochs of training, the combined model generates responses comparable in quality to those obtained by distillation. We discuss the advantages of the modular approach, provide examples of input queries and comparative analysis, and outline prospects for further extension of the method.