LLaMaFlex: Many-in-one LLMs via Generalized Pruning and Weight Sharing
Ruisi Cai Saurav Muralidharan Hongxu Yin Zhangyang Wang Jan Kautz Pavlo Molchanov
Large Language Model (LLM) providers typically train a family of models, each of a different size targeting a specific deployment scenario. Models in the family are all trained from scratch, making the process extremely resource intensive.
Recent work has successfully reduced the cost of training model families through a combination of structured pruning and knowledge distillation; here, only the largest model in the family is trained from scratch, and smaller models are obtained via pruning. We observe that while effective, this strategy must still perform pruning and distillation with hundreds of billions of training tokens for every new model, keeping overall training costs high.
In this work, we introduce a novel nested weight-shared architecture named LLaMaFlex that can be pruned across both width and depth dimensions in a zero-shot manner to instantly yield a large number of highly accurate compressed models.
LLaMaFlex starts from a pretrained model, and only requires a single continued training phase consisting of ~60B tokens, which trains the elastic network and an end-to-end Gumbel Softmax-based router; this router is able to interpolate smoothly across model sizes, enabling the "train once, deploy many'' paradigm.
We train LLaMaFlex on Llama 3.1 8B and use it to zero-shot generate a family of compressed models that achieves accuracy on par with or better than state-of-the-art pruned, elastic/flexible, and trained-from-scratch models.
large language models, elastic networks, training efficiency, inference efficiency