EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit Large Language Models

• URL: http://arxiv.org/abs/2402.00518v1
• Date: Thu, 1 Feb 2024 11:39:04 GMT
• Title: EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit Large Language Models
• Authors: Xuchen Pan, Yanxi Chen, Yaliang Li, Bolin Ding, Jingren Zhou
• Abstract summary: EE-Tuning is a solution to training/tuning early-exit large language models (LLMs) It augments any pre-trained (and possibly fine-tuned) standard LLM with additional early-exit layers that are tuned in a parameter-efficient manner. Our implementation achieves outstanding training efficiency via extensive performance optimizations.
• Score: 75.1814102438065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work introduces EE-Tuning, a lightweight and economical solution to training/tuning early-exit large language models (LLMs). In contrast to the common approach of full-parameter pre-training, EE-Tuning augments any pre-trained (and possibly fine-tuned) standard LLM with additional early-exit layers that are tuned in a parameter-efficient manner, which requires significantly less computational resources and training data. Our implementation of EE-Tuning achieves outstanding training efficiency via extensive performance optimizations, as well as scalability due to its full compatibility with 3D parallelism. Results of systematic experiments validate the efficacy of EE-Tuning, confirming that effective early-exit LLM inference can be achieved with a limited training budget. In hope of making early-exit LLMs accessible to the community, we release the source code of our implementation of EE-Tuning at https://github.com/pan-x-c/EE-LLM.

Related papers

• The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities [0.35998666903987897]
  This report examines the fine-tuning of Large Language Models (LLMs) It outlines the historical evolution of LLMs from traditional Natural Language Processing (NLP) models to their pivotal role in AI. The report introduces a structured seven-stage pipeline for fine-tuning LLMs.
  arXiv  Detail & Related papers  (2024-08-23T14:48:02Z)
• HFT: Half Fine-Tuning for Large Language Models [42.60438623804577]
  Large language models (LLMs) with one or more fine-tuning phases have become a necessary step to unlock various capabilities. In this paper, we find that by regularly resetting partial parameters, LLMs can restore some of the original knowledge. We introduce Half Fine-Tuning (HFT) for LLMs, as a substitute for full fine-tuning (FFT), to mitigate the forgetting issues.
  arXiv  Detail & Related papers  (2024-04-29T07:07:58Z)
• Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models [90.14693869269519]
  MoE LLMs can achieve higher performance with fewer parameters, but it is still hard to deploy them due to their immense parameter sizes. This paper mainly aims to enhance the deployment efficiency of MoE LLMs by introducing plug-and-play expert-level sparsification techniques.
  arXiv  Detail & Related papers  (2024-02-22T18:56:07Z)
• EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism [70.07661254213181]
  We present EE-LLM, a framework for large-scale training and inference of early-exit large language models (LLMs) Built upon Megatron-LM, EE-LLM implements a variety of algorithmic innovations and performance optimizations tailored to early exiting. Our analytical and empirical study shows that EE-LLM achieves great training efficiency with negligible computational overhead.
  arXiv  Detail & Related papers  (2023-12-08T09:31:50Z)
• 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)
• Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization [71.87335804334616]
  Federated learning (FL) is a promising paradigm to enable collaborative model training with decentralized data. The training process of Large Language Models (LLMs) generally incurs the update of significant parameters. This paper proposes an efficient partial prompt tuning approach to improve performance and efficiency simultaneously.
  arXiv  Detail & Related papers  (2023-10-23T16:37:59Z)
• FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning [70.38817963253034]
  This paper first discusses these challenges of federated fine-tuning LLMs, and introduces our package FS-LLM as a main contribution. We provide comprehensive federated parameter-efficient fine-tuning algorithm implementations and versatile programming interfaces for future extension in FL scenarios. We conduct extensive experiments to validate the effectiveness of FS-LLM and benchmark advanced LLMs with state-of-the-art parameter-efficient fine-tuning algorithms in FL settings.
  arXiv  Detail & Related papers  (2023-09-01T09:40:36Z)

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.