  Large Language Models (LLMs) such as ChatGPT and LlaMA are advancing rapidly
in generative Artificial Intelligence (AI), but their immense size poses
significant challenges, such as huge training and inference costs, substantial
energy demands, and limitations for on-site deployment. Traditional compression
methods such as pruning, distillation, and low-rank approximation focus on
reducing the effective number of neurons in the network, while quantization
focuses on reducing the numerical precision of individual weights to reduce the
model size while keeping the number of neurons fixed. While these compression
methods have been relatively successful in practice, there is no compelling
reason to believe that truncating the number of neurons is an optimal strategy.
In this context, this paper introduces CompactifAI, an innovative LLM
compression approach using quantum-inspired Tensor Networks that focuses on the
model's correlation space instead, allowing for a more controlled, refined and
interpretable model compression. Our method is versatile and can be implemented
with - or on top of - other compression techniques. As a benchmark, we
demonstrate that a combination of CompactifAI with quantization allows to
reduce a 93% the memory size of LlaMA 7B, reducing also 70% the number of
parameters, accelerating 50% the training and 25% the inference times of the
model, and just with a small accuracy drop of 2% - 3%, going much beyond of
what is achievable today by other compression techniques. Our methods also
allow to perform a refined layer sensitivity profiling, showing that deeper
layers tend to be more suitable for tensor network compression, which is
compatible with recent observations on the ineffectiveness of those layers for
LLM performance. Our results imply that standard LLMs are, in fact, heavily
overparametrized, and do not need to be large at all.
