LIBMoE: A Library for comprehensive benchmarking Mixture of Experts in Large Language Models

• URL: http://arxiv.org/abs/2411.00918v1
• Date: Fri, 01 Nov 2024 14:04:36 GMT
• Title: LIBMoE: A Library for comprehensive benchmarking Mixture of Experts in Large Language Models
• Authors: Nam V. Nguyen, Thong T. Doan, Luong Tran, Van Nguyen, Quang Pham,
• Abstract summary: emphLibMoE is a comprehensive framework to streamline the research, training, and evaluation of MoE algorithms. LibMoE brings MoE in large language models (LLMs) more accessible to a wide range of researchers by standardizing the training and evaluation pipelines.
• Score: 7.164238322896674
• License:
• Abstract: Mixture of Experts (MoEs) plays an important role in the development of more efficient and effective large language models (LLMs). Due to the enormous resource requirements, studying large scale MoE algorithms remain in-accessible to many researchers. This work develops \emph{LibMoE}, a comprehensive and modular framework to streamline the research, training, and evaluation of MoE algorithms. Built upon three core principles: (i) modular design, (ii) efficient training; (iii) comprehensive evaluation, LibMoE brings MoE in LLMs more accessible to a wide range of researchers by standardizing the training and evaluation pipelines. Using LibMoE, we extensively benchmarked five state-of-the-art MoE algorithms over three different LLMs and 11 datasets under the zero-shot setting. The results show that despite the unique characteristics, all MoE algorithms perform roughly similar when averaged across a wide range of tasks. With the modular design and extensive evaluation, we believe LibMoE will be invaluable for researchers to make meaningful progress towards the next generation of MoE and LLMs. Project page: \url{https://fsoft-aic.github.io/fsoft-LibMoE.github.io}.

Related papers

• Mixture of Modular Experts: Distilling Knowledge from a Multilingual Teacher into Specialized Modular Language Models [0.6372911857214884]
  This research combines Knowledge Distillation (KD) and Mixture of Experts (MoE) to develop modular, efficient multilingual language models. Key objectives include evaluating adaptive versus fixed alpha methods in KD and comparing modular MoE architectures for handling multi-domain inputs.
  arXiv  Detail & Related papers  (2024-07-28T23:42:09Z)
• LLMBox: A Comprehensive Library for Large Language Models [109.15654830320553]
  This paper presents a comprehensive and unified library, LLMBox, to ease the development, use, and evaluation of large language models (LLMs) This library is featured with three main merits: (1) a unified data interface that supports the flexible implementation of various training strategies, (2) a comprehensive evaluation that covers extensive tasks, datasets, and models, and (3) more practical consideration, especially on user-friendliness and efficiency.
  arXiv  Detail & Related papers  (2024-07-08T02:39:33Z)
• A Survey on Mixture of Experts [11.801185267119298]
  The mixture of experts (MoE) has emerged as an effective method for substantially scaling up model capacity with minimal overhead. MoE has emerged as an effective method for substantially scaling up model capacity with minimal overhead. This survey seeks to bridge that gap, serving as an essential resource for researchers delving into the intricacies of MoE.
  arXiv  Detail & Related papers  (2024-06-26T16:34:33Z)
• A Closer Look into Mixture-of-Experts in Large Language Models [26.503570706063634]
  Mixture-of-experts (MoE) is gaining increasing attention due to its unique properties and remarkable performance. MoE architecture could increase the model size without sacrificing computational efficiency. We make an initial attempt to understand the inner workings of MoE-based large language models.
  arXiv  Detail & Related papers  (2024-06-26T10:07:57Z)
• UltraEval: A Lightweight Platform for Flexible and Comprehensive Evaluation for LLMs [74.1976921342982]
  This paper introduces UltraEval, a user-friendly evaluation framework characterized by its lightweight nature, comprehensiveness, modularity, and efficiency. The resulting composability allows for the free combination of different models, tasks, prompts, benchmarks, and metrics within a unified evaluation workflow.
  arXiv  Detail & Related papers  (2024-04-11T09:17:12Z)
• OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models [44.848642930797155]
  We release OpenMoE, a series of fully open-sourced and reproducible decoder-only Mixture-of-Experts (MoE) based large language models (LLMs) Our investigation confirms that MoE-based LLMs can offer a more favorable cost-effectiveness trade-off than dense LLMs. We find that routing decisions in MoE models are predominantly based on token IDs, with minimal context relevance.
  arXiv  Detail & Related papers  (2024-01-29T12:05:02Z)
• CoLLiE: Collaborative Training of Large Language Models in an Efficient Way [59.09824823710863]
  CoLLiE is an efficient library that facilitates collaborative training of large language models. With its modular design and comprehensive functionality, CoLLiE offers a balanced blend of efficiency, ease of use, and customization.
  arXiv  Detail & Related papers  (2023-12-01T08:02:16Z)
• Octavius: Mitigating Task Interference in MLLMs via LoRA-MoE [85.76186554492543]
  Large Language Models (LLMs) can extend their zero-shot capabilities to multimodal learning through instruction tuning. negative conflicts and interference may have a worse impact on performance. We propose a novel framework, called Octavius, for comprehensive studies and experimentation on multimodal learning with MLLMs.
  arXiv  Detail & Related papers  (2023-11-05T15:48:29Z)
• MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models [73.86954509967416]
  Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform multimodal tasks. This paper presents the first comprehensive MLLM Evaluation benchmark MME. It measures both perception and cognition abilities on a total of 14 subtasks.
  arXiv  Detail & Related papers  (2023-06-23T09:22:36Z)
• FedML: A Research Library and Benchmark for Federated Machine Learning [55.09054608875831]
  Federated learning (FL) is a rapidly growing research field in machine learning. Existing FL libraries cannot adequately support diverse algorithmic development. We introduce FedML, an open research library and benchmark to facilitate FL algorithm development and fair performance comparison.
  arXiv  Detail & Related papers  (2020-07-27T13:02:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.