Mixtral of experts

AQ Jiang, A Sablayrolles, A Roux, A Mensch… - arXiv preprint arXiv …, 2024 - arxiv.org
Mixtral 8x7B, a sparse mixture of experts model (SMoE) with open weights, licensed under
Apache 2.0. Mixtral … For Mixtral we use the same SwiGLU architecture as the expert function Ei…

[HTML][HTML] Mixtral of Experts

S Teki - sundeepteki.org
… 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 …

A closer look into mixture-of-experts in large language models

KM Lo, Z Huang, Z Qiu, Z Wang, J Fu - arXiv preprint arXiv:2406.18219, 2024 - arxiv.org
… observed certain characteristicss shared by Mixtral experts (eg, relatively high similarities
of weight matrices), and a notable relationship between these experts and the Mistral FFN (eg, …

Efficient mixture of experts based on large language models for low-resource data preprocessing

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

Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

X Lu, Q Liu, Y Xu, A Zhou, S Huang, B Zhang… - arXiv preprint arXiv …, 2024 - arxiv.org
… 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

Mc-moe: Mixture compressor for mixture-of-experts llms gains more

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 …

Mixture of experts with mixture of precisions for tuning quality of service

HR Imani, A Amirany, T El-Ghazawi - arXiv preprint arXiv:2407.14417, 2024 - arxiv.org
… 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. …

Efficient expert pruning for sparse mixture-of-experts language models: Enhancing performance and reducing inference costs

E Liu, J Zhu, Z Lin, X Ning, MB Blaschko, S Yan… - arXiv preprint arXiv …, 2024 - arxiv.org
… 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 …

Fast inference of mixture-of-experts language models with offloading

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 …

Examining Post-Training Quantization for Mixture-of-Experts: A Benchmark

P Li, X Jin, Y Cheng, T Chen - arXiv preprint arXiv:2406.08155, 2024 - arxiv.org
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-…