LLM Modules: Knowledge Transfer from a Large to a Small Model using
  Enhanced Cross-Attention
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.
67ad7c47565fbcfa66777f5a
Konstantin Kolomeitsev
claimed_verified
2025-02-13T08:21:29.819Z
63df748df0c75dfb87621f93
https://cdn-avatars.huggingface.co/v1/production/uploads/63df748df0c75dfb87621f93/Hzc3Du2BqnTP_8R9JsP-4.jpeg
Konstantin Kolomeitsev
user
kkolomeitsev
LLM Modules: Knowledge Transfer from a Large to a Small Model using
  Enhanced Cross-Attention
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.
A large pre-trained model transfers knowledge to a smaller model via an enhanced cross-attention mechanism, achieving quality comparable to distillation after minimal training.
LLM Modules Enhanced Cross-Attention Qwen2-1.5B GPT-Neo-125M Bespoke-Stratos-17k dataset