AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment
- URL: http://arxiv.org/abs/2411.10606v1
- Date: Fri, 15 Nov 2024 22:02:28 GMT
- Title: AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment
- Authors: Yonggan Fu, Zhongzhi Yu, Junwei Li, Jiayi Qian, Yongan Zhang, Xiangchi Yuan, Dachuan Shi, Roman Yakunin, Yingyan Celine Lin,
- Abstract summary: AmoebaLLM is a novel framework designed to enable the instant derivation of large language models of arbitrary shapes.
AmoebaLLM significantly facilitates rapid deployment tailored to various platforms and applications.
- Score: 13.977849745488339
- License:
- Abstract: Motivated by the transformative capabilities of large language models (LLMs) across various natural language tasks, there has been a growing demand to deploy these models effectively across diverse real-world applications and platforms. However, the challenge of efficiently deploying LLMs has become increasingly pronounced due to the varying application-specific performance requirements and the rapid evolution of computational platforms, which feature diverse resource constraints and deployment flows. These varying requirements necessitate LLMs that can adapt their structures (depth and width) for optimal efficiency across different platforms and application specifications. To address this critical gap, we propose AmoebaLLM, a novel framework designed to enable the instant derivation of LLM subnets of arbitrary shapes, which achieve the accuracy-efficiency frontier and can be extracted immediately after a one-time fine-tuning. In this way, AmoebaLLM significantly facilitates rapid deployment tailored to various platforms and applications. Specifically, AmoebaLLM integrates three innovative components: (1) a knowledge-preserving subnet selection strategy that features a dynamic-programming approach for depth shrinking and an importance-driven method for width shrinking; (2) a shape-aware mixture of LoRAs to mitigate gradient conflicts among subnets during fine-tuning; and (3) an in-place distillation scheme with loss-magnitude balancing as the fine-tuning objective. Extensive experiments validate that AmoebaLLM not only sets new standards in LLM adaptability but also successfully delivers subnets that achieve state-of-the-art trade-offs between accuracy and efficiency.
Related papers
- Transformer-Squared: Self-adaptive LLMs [29.1326358746118]
We introduce Transformer-Squared, a novel self-adaptation framework that adapts large language models for unseen tasks in real-time.
Our method consistently outperforms ubiquitous approaches such as LoRA, with fewer parameters and greater efficiency.
Transformer-Squared represents a significant leap forward, offering a scalable, efficient solution for enhancing the adaptability and task-specific performance of LLMs.
arXiv Detail & Related papers (2025-01-09T01:19:21Z) - LSAQ: Layer-Specific Adaptive Quantization for Large Language Model Deployment [13.235417359529965]
We propose LSAQ (Layer-Specific Adaptive Quantization), a system for adaptive quantization and dynamic deployment of large language models (LLMs) based on layer importance.
The system adaptively adjusts quantization strategies in real time according to the resource availability of edge devices, assigning different precision levels to layers of varying importance.
arXiv Detail & Related papers (2024-12-24T03:43:15Z) - Adaptive Pruning for Large Language Models with Structural Importance Awareness [66.2690963378878]
Large language models (LLMs) have significantly improved language understanding and generation capabilities.
LLMs are difficult to deploy on resource-constrained edge devices due to their high computational and storage resource demands.
We propose structurally-aware adaptive pruning (SAAP) to significantly reduce the computational and memory costs while maintaining model performance.
arXiv Detail & Related papers (2024-12-19T18:08:04Z) - GUIDE: A Global Unified Inference Engine for Deploying Large Language Models in Heterogeneous Environments [1.0558515062670693]
Large language models (LLMs) in real-world scenarios remains a critical challenge.
These challenges often lead to inefficiencies in memory utilization, latency, and throughput.
We develop a framework to address these issues, achieving prediction errors between 9.9% and 42.3% for key metrics such as batch latency, TTFT, and decode throughput.
arXiv Detail & Related papers (2024-12-06T05:46:43Z) - Read-ME: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design [59.00758127310582]
We propose a novel framework Read-ME that transforms pre-trained dense LLMs into smaller MoE models.
Our approach employs activation sparsity to extract experts.
Read-ME outperforms other popular open-source dense models of similar scales.
arXiv Detail & Related papers (2024-10-24T19:48:51Z) - LLaVA-KD: A Framework of Distilling Multimodal Large Language Models [70.19607283302712]
We propose a novel framework to transfer knowledge from l-MLLM to s-MLLM.
Specifically, we introduce Multimodal Distillation (MDist) to minimize the divergence between the visual-textual output distributions of l-MLLM and s-MLLM.
We also propose a three-stage training scheme to fully exploit the potential of s-MLLM.
arXiv Detail & Related papers (2024-10-21T17:41:28Z) - Duo-LLM: A Framework for Studying Adaptive Computation in Large Language Models [16.16372459671255]
Large Language Models (LLMs) typically generate outputs token by token using a fixed compute budget.
We propose a novel framework that integrates smaller auxiliary modules within each Feed-Forward Network layer of the LLM.
We show that trained routers operate differently from oracles and often yield suboptimal solutions.
arXiv Detail & Related papers (2024-10-01T16:10:21Z) - Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities.
In-Context Learning (ICL) and.
Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting.
LLMs to downstream tasks.
We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
arXiv Detail & Related papers (2024-09-30T10:48:20Z) - MoExtend: Tuning New Experts for Modality and Task Extension [61.29100693866109]
MoExtend is an effective framework designed to streamline the modality adaptation and extension of Mixture-of-Experts (MoE) models.
MoExtend seamlessly integrates new experts into pre-trained MoE models, endowing them with novel knowledge without the need to tune pretrained models.
arXiv Detail & Related papers (2024-08-07T02:28:37Z) - DOLLmC: DevOps for Large Language model Customization [0.0]
This research aims to establish a scalable and efficient framework for LLM customization.
We propose a robust framework that enhances continuous learning, seamless deployment, and rigorous version control of LLMs.
arXiv Detail & Related papers (2024-05-19T15:20:27Z) - CREMA: Generalizable and Efficient Video-Language Reasoning via Multimodal Modular Fusion [58.15403987979496]
CREMA is a generalizable, highly efficient, and modular modality-fusion framework for video reasoning.
We propose a novel progressive multimodal fusion design supported by a lightweight fusion module and modality-sequential training strategy.
We validate our method on 7 video-language reasoning tasks assisted by diverse modalities, including VideoQA and Video-Audio/3D/Touch/Thermal QA.
arXiv Detail & Related papers (2024-02-08T18:27:22Z)
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.