… While GPT4 is said to constitute 8 expert models of 222B parameters each, Mixtral is a mixture of 8 experts of 7B parameters each. Thus, Mixtral only requires a subset of the total …
… observed certain characteristicss shared by Mixtralexperts (eg, relatively high similarities of weight matrices), and a notable relationship between these experts and the Mistral FFN (eg, …
M Yan, Y Wang, K Pang, M Xie, J Li - Proceedings of the 30th ACM …, 2024 - dl.acm.org
… layer, we can see that Mixtral outperforms MELD in a few tasks … Mixtral fails to apply a good routing strategy, and Mixtral does not balance the load well for the task family T to its 8 experts…
… We perform expert pruning on both Mixtral 8x7B and Mixtral 8x7B Instruct models, resulting … experts discarded (r = 6) and four experts discarded (r = 4) in each layer. Pruning a Mixtral …
W Huang, Y Liao, J Liu, R He, H Tan, S Zhang… - arXiv preprint arXiv …, 2024 - arxiv.org
… is to reduce the size of expert parameters, as they dominate … For instance, in models like Mixtral 8 × 7b, the number of expert … In Mixtral 8 × 7b, we mask all experts of the less significant …
… Authors explore the decrease in the output quality of a Mixtral MoE model caused by expert … using a Mixtral 8x7B MoE model [10], which consists of 32 layers and 8 experts per layer. …
… For example, we demonstrate that pruning up to 75% of experts in Mixtral 8 × 7B-Instruct results in a substantial reduction in parameters with minimal performance loss. Remarkably, we …
A Eliseev, D Mazur - arXiv preprint arXiv:2312.17238, 2023 - arxiv.org
… If we examine popular open-access MoE models (Mixtral-8x7B and switch-c-2048), we find that all non-experts can fit a fraction of available GPU memory. In turn, the experts that …
… Mixtral-8x7B model, we compare the allocation of 4 bits to the top-{2, 4} most frequently used experts … We evaluate it on the top-2 most frequently used experts per MoE block in Mixtral-…