Efficient Large Language Models: A Survey [arXiv] (Version 1: 12/06/2023; Version 2: 12/23/2023; Version 3: 01/31/2024; Version 4: 05/23/2024, camera ready version of Transactions on Machine Learning Research)
Zhongwei Wan1, Xin Wang1, Che Liu2, Samiul Alam1, Yu Zheng3, Jiachen Liu4, Zhongnan Qu5, Shen Yan6, Yi Zhu7, Quanlu Zhang8, Mosharaf Chowdhury4, Mi Zhang1
1The Ohio State University, 2Imperial College London, 3Michigan State University, 4University of Michigan, 5Amazon AWS AI, 6Google Research, 7Boson AI, 8Microsoft Research Asia
β‘News: Our survey has been officially accepted by Transactions on Machine Learning Research (TMLR) 2024. Camera ready version is available at: [OpenReview]
@article{wan2023efficient,
title={Efficient large language models: A survey},
author={Wan, Zhongwei and Wang, Xin and Liu, Che and Alam, Samiul and Zheng, Yu and others},
journal={arXiv preprint arXiv:2312.03863},
volume={1},
year={2023},
publisher={no}
}
This repository is maintained by tuidan (wang.15980@osu.edu), SUSTechBruce (wan.512@osu.edu), samiul272 (alam.140@osu.edu), and mi-zhang (mizhang.1@osu.edu). We welcome feedback, suggestions, and contributions that can help improve this survey and repository so as to make them valuable resources to benefit the entire community.
We will actively maintain this repository by incorporating new research as it emerges. If you have any suggestions regarding our taxonomy, find any missed papers, or update any preprint arXiv paper that has been accepted to some venue, feel free to send us an email or submit a pull request using the following markdown format.
Paper Title, <ins>Conference/Journal/Preprint, Year</ins> [[pdf](link)] [[other resources](link)].
Large Language Models (LLMs) have demonstrated remarkable capabilities in many important tasks and have the potential to make a substantial impact on our society. Such capabilities, however, come with considerable resource demands, highlighting the strong need to develop effective techniques for addressing the efficiency challenges posed by LLMs. In this survey, we provide a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from model-centric, data-centric, and framework-centric perspective, respectively. We hope our survey and this GitHub repository can serve as valuable resources to help researchers and practitioners gain a systematic understanding of the research developments in efficient LLMs and inspire them to contribute to this important and exciting field.
Although LLMs are leading the next wave of AI revolution, the remarkable capabilities of LLMs come at the cost of their substantial resource demands. Figure 1 (left) illustrates the relationship between model performance and model training time in terms of GPU hours for LLaMA series, where the size of each circle is proportional to the number of model parameters. As shown, although larger models are able to achieve better performance, the amounts of GPU hours used for training them grow exponentially as model sizes scale up. In addition to training, inference also contributes quite significantly to the operational cost of LLMs. Figure 2 (right) depicts the relationship between model performance and inference throughput. Similarly, scaling up the model size enables better performance but comes at the cost of lower inference throughput (higher inference latency), presenting challenges for these models in expanding their reach to a broader customer base and diverse applications in a cost-effective way. The high resource demands of LLMs highlight the strong need to develop techniques to enhance the efficiency of LLMs. As shown in Figure 2, compared to LLaMA-1-33B, Mistral-7B, which uses grouped-query attention and sliding window attention to speed up inference, achieves comparable performance and much higher throughput. This superiority highlights the feasibility and significance of designing efficiency techniques for LLMs.
- π€ Model-Centric Methods
- π’ Data-Centric Methods
- π§βπ» System-Level Efficiency Optimization and LLM Frameworks
- I-LLM: Efficient Integer-Only Inference for Fully-Quantized Low-Bit Large Language Models, arXiv, 2024 [Paper]
- IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact, arXiv, 2024 [Paper]
- OmniQuant: OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models, ICLR, 2024 [Paper] [Code]
- OneBit: Towards Extremely Low-bit Large Language Models, arXiv, 2024 [Paper]
- GPTQ: Accurate Quantization for Generative Pre-trained Transformers, ICLR, 2023 [Paper] [Code]
- QuIP: 2-Bit Quantization of Large Language Models With Guarantees, arXiv, 2023 [Paper] [Code]
- AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration, arXiv, 2023 [Paper] [Code]
- OWQ: Lessons Learned from Activation Outliers for Weight Quantization in Large Language Models, arXiv, 2023 [Paper] [Code]
- SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression, arXiv, 2023 [Paper] [Code]
- FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only Quantization for LLMs, NeurIPS-ENLSP, 2023 [Paper]
- LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale, NeurlPS, 2022 [Paper] [Code]
- Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning, NeurIPS, 2022 [Paper] [Code]
- QuantEase: Optimization-based Quantization for Language Models, arXiv, 2023 [Paper] [Code]
- OmniQuant: OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models, ICLR, 2024 [Paper] [Code]
- Intriguing Properties of Quantization at Scale, NeurIPS, 2023 [Paper]
- ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation, arXiv, 2023 [Paper] [Code]
- ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats, NeurIPS-ENLSP, 2023 [Paper] [Code]
- OliVe: Accelerating Large Language Models via Hardware-friendly Outlier-Victim Pair Quantization, ISCA, 2023 [Paper] [Code]
- RPTQ: Reorder-based Post-training Quantization for Large Language Models, arXiv, 2023 [Paper] [Code]
- Outlier Suppression+: Accurate Quantization of Large Language Models by Equivalent and Optimal Shifting and Scaling, arXiv, 2023 [Paper] [Code]
- QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models, arXiv, 2023 [Paper]
- SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models, ICML, 2023 [Paper] [Code]
- ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers, NeurIPS, 2022 [Paper]
- Evaluating Quantized Large Language Models, arXiv, 2024 [Paper]
- The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits, arXiv, 2024 [Paper]
- FP8-LM: Training FP8 Large Language Models, arXiv, 2023 [Paper]
- Training and inference of large language models using 8-bit floating point, arXiv, 2023 [Paper]
- BitNet: Scaling 1-bit Transformers for Large Language Models, arXiv, 2023 [Paper]
- LLM-QAT: Data-Free Quantization Aware Training for Large Language Models, arXiv, 2023 [Paper] [Code]
- Compression of Generative Pre-trained Language Models via Quantization, ACL, 2022 [Paper]
- A deeper look at depth pruning of LLMs, arXiv, 2024 [Paper]
- Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models, arXiv, 2024 [Paper]
- Plug-and-Play: An Efficient Post-training Pruning Method for Large Language Models, ICLR, 2024 [Paper]
- BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation, arXiv, 2024 [Paper]
- ShortGPT: Layers in Large Language Models are More Redundant Than You Expect, arXiv, 2024 [Paper]
- NutePrune: Efficient Progressive Pruning with Numerous Teachers for Large Language Models, arXiv, 2024 [Paper]
- SliceGPT: Compress Large Language Models by Deleting Rows and Columns, ICLR, 2024 [Paper] [Code]
- LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery, arXiv, 2023 [Paper]
- LLM-Pruner: On the Structural Pruning of Large Language Models, NeurIPS, 2023 [Paper] [Code]
- Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning, Β NeurIPS-ENLSP, 2023 [Paper] [Code]
- LoRAPrune: Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning, arXiv, 2023 [Paper]
- Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs, ICLR, 2024 [Paper]
- SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot, ICML, 2023 [Paper] [Code]
- A Simple and Effective Pruning Approach for Large Language Models, arXiv, 2023 [Paper] [Code]
- One-Shot Sensitivity-Aware Mixed Sparsity Pruning for Large Language Models, arXiv, 2023 [Paper]
- SVD-LLM: Singular Value Decomposition for Large Language Model Compression, arXiv, 2024 [Paper] [Code]
- ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models, arXiv, 2023 [Paper] [Code]
- Language model compression with weighted low-rank factorization, ICLR, 2022 [Paper]
- TensorGPT: Efficient Compression of the Embedding Layer in LLMs based on the Tensor-Train Decomposition, arXiv, 2023 [Paper]
- LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation, ICML, 2023 [Paper] [Code]
- Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models arXiv, 2024 [Paper]
- DistiLLM: Towards Streamlined Distillation for Large Language Models, arXiv, 2024 [Paper] [Code]
- Towards the Law of Capacity Gap in Distilling Language Models, arXiv, 2023 [Paper] [Code]
- Baby Llama: Knowledge Distillation from an Ensemble of Teachers Trained on a Small Dataset with no Performance Penalty, arXiv, 2023 [Paper]
- Knowledge Distillation of Large Language Models, arXiv, 2023 [Paper] [Code]
- GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models, arXiv, 2023 [Paper]
- Propagating Knowledge Updates to LMs Through Distillation, arXiv, 2023 [Paper] [Code]
- Less is More: Task-aware Layer-wise Distillation for Language Model Compression, ICML, 2023 [Paper]
- Token-Scaled Logit Distillation for Ternary Weight Generative Language Models, arXiv, 2023 [Paper]
- Zephyr: Direct Distillation of LM Alignment, arXiv, 2023 [Paper]
- Instruction Tuning with GPT-4, arXiv, 2023 [Paper] [Code]
- Lion: Adversarial Distillation of Closed-Source Large Language Model, arXiv, 2023 [Paper] [Code]
- Specializing Smaller Language Models towards Multi-Step Reasoning, ICML, 2023 [Paper] [Code]
- Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes, ACL, 2023 [Paper]
- Large Language Models Are Reasoning Teachers, ACL, 2023 [Paper] [Code]
- SCOTT: Self-Consistent Chain-of-Thought Distillation, ACL, 2023 [Paper] [Code]
- Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step, ACL, 2023 [Paper]
- Distilling Reasoning Capabilities into Smaller Language Models, ACL, 2023 [Paper] [Code]
- In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models, arXiv, 2022 [Paper]
- Explanations from Large Language Models Make Small Reasoners Better, arXiv, 2022 [Paper]
- DISCO: Distilling Counterfactuals with Large Language Models, arXiv, 2022 [Paper] [Code]
- MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT, arXiv, 2024 [Paper]
- Bfloat16 Processing for Neural Networks, ARITH, 2019 [Paper]
- A Study of BFLOAT16 for Deep Learning Training, arXiv, 2019 [Paper]
- Mixed Precision Training, ICLR, 2018 [Paper]
- lemon: lossless model expansion, ICLR, 2024 [Paper]
- Preparing Lessons for Progressive Training on Language Models, AAAI, 2024 [Paper]
- Learning to Grow Pretrained Models for Efficient Transformer Training, ICLR, 2023 [Paper] [Code]
- 2x Faster Language Model Pre-training via Masked Structural Growth, arXiv, 2023 [Paper]
- Reusing Pretrained Models by Multi-linear Operators for Efficient Training, NeurIPS, 2023 [Paper]
- FLM-101B: An Open LLM and How to Train It with $100 K Budget, arXiv, 2023 [Paper] [Code]
- Knowledge Inheritance for Pre-trained Language Models, NAACL, 2022 [Paper] [Code]
- Staged Training for Transformer Language Models, ICML, 2022 [Paper] [Code]
- Deepnet: Scaling transformers to 1,000 layers, arXiv, 2022 [Paper] [Code]
- ZerO Initialization: Initializing Neural Networks with only Zeros and Ones, TMLR, 2022 [Paper] [Code]
- Rezero is All You Need: Fast Convergence at Large Depth, UAI, 2021 [Paper] [Code]
- Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks, NeurIPS, 2020 [Paper]
- Improving Transformer Optimization Through Better Initialization, ICML, 2020 [Paper] [Code]
- Fixup Initialization: Residual Learning without Normalization, ICLR, 2019 [Paper]
- On Weight Initialization in Deep Neural Networks, arXiv, 2017 [Paper]
- Towards Optimal Learning of Language Models, arXiv, 2024 [Paper] [Code]
- Symbolic Discovery of Optimization Algorithms, arXiv, 2023 [Paper]
- Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training, arXiv, 2023 [Paper] [Code]
- OpenDelta: A Plug-and-play Library for Parameter-efficient Adaptation of Pre-trained Models, ACL Demo, 2023 [Paper] [Code]
- LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models, EMNLP, 2023 [Paper] [Code]
- Compacter: Efficient Low-Rank Hypercomplex Adapter Layers, NeurIPS, 2023 [Paper] [Code]
- Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning, NeurIPS, 2022 [Paper] [Code]
- Meta-Adapters: Parameter Efficient Few-shot Fine-tuning through Meta-Learning, AutoML, 2022 [Paper]
- AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning, EMNLP, 2022 [Paper] [Code]
- SparseAdapter: An Easy Approach for Improving the Parameter-Efficiency of Adapters, EMNLP, 2022 [Paper] [Code]
- LOFIT: Localized Fine-tuning on LLM Representations, Arxiv, 2024 [Paper]
- Mixture-of-Subspaces in Low-Rank Adaptation, Arxiv, 2024 [Paper] [Code]
- MEFT: Memory-Efficient Fine-Tuning through Sparse Adapter, ACL, 2024 [Paper]
- LoRA Meets Dropout under a Unified Framework, arXiv, 2024 [Paper]
- STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Models, arXiv, 2024 [Paper]
- LoRA+: Efficient Low Rank Adaptation of Large Models, arXiv, 2024 [Paper]
- LoRA-FA: Memory-efficient Low-rank Adaptation for Large Language Models Fine-tuning, arXiv, 2023 [Paper]
- LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition, arXiv, 2023 [Paper] [Code]
- LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models, arXiv, 2023 [Paper] [Code]
- Multi-Head Adapter Routing for Cross-Task Generalization, NeurIPS, 2023 [Paper] [Code]
- Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning, ICLR, 2023 [Paper]
- DyLoRA: Parameter-Efficient Tuning of Pretrained Models using Dynamic Search-Free Low Rank Adaptation, EACL, 2023 [Paper] [Code]
- Tied-Lora: Enhacing Parameter Efficiency of LoRA with Weight Tying, arXiv, 2023 [Paper]
- LoRA: Low-Rank Adaptation of Large Language Models, ICLR, 2022 [Paper] [Code]
- LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention, arXiv, 2023 [Paper] [Code]
- Prefix-Tuning: Optimizing Continuous Prompts for Generation ACL, 2021 [Paper] [Code]
- Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt, arXiv, 2023 [Paper]
- GPT Understands, Too, AI Open, 2023 [Paper] [Code]
- Multi-Task Pre-Training of Modular Prompt for Few-Shot Learning ACL, 2023 [Paper] [Code]
- Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning, ICLR, 2023 [Paper]
- PPT: Pre-trained Prompt Tuning for Few-shot Learning, ACL, 2022 [Paper] [Code]
- Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers, EMNLP-Findings, 2022 [Paper] [Code]
- P-Tuning v2: Prompt Tuning Can Be Comparable to Finetuning Universally Across Scales and TasksοΌACL-Short, 2022 [Paper] [Code]
- The Power of Scale for Parameter-Efficient Prompt Tuning, EMNLP, 2021 [Paper]
- A Study of Optimizations for Fine-tuning Large Language Models, arXiv, 2024/ins> [Paper]
- Sparse Matrix in Large Language Model Fine-tuning, arXiv, 2024/ins> [Paper]
- GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection, arXiv, 2024/ins> [Paper]
- ReFT: Representation Finetuning for Language Models, arXiv, 2024/ins> [Paper]
- LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning, arXiv, 2024/ins> [Paper]
- BitDelta: Your Fine-Tune May Only Be Worth One Bit, arXiv, 2024/ins> [Paper]
- Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model, NeurIPS, 2023 [Paper] [Code]
- Memory-Efficient Selective Fine-Tuning, ICML Workshop, 2023 [Paper]
- Full Parameter Fine-tuning for Large Language Models with Limited Resources, arXiv, 2023 [Paper] [Code]
- Fine-Tuning Language Models with Just Forward Passes, NeurIPS, 2023 [Paper] [Code]
- Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization, NeurIPS, 2023 [Paper]
- LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models, arXiv, 2023 [Paper] [Code]
- QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models, arXiv, 2023 [Paper] [Code]
- QLoRA: Efficient Finetuning of Quantized LLMs, NeurIPS, 2023 [Paper] [Code1] [Code2]
- Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models, arXiv, 2024 [Paper]
- CLLMs: Consistency Large Language Models, arXiv, 2024 [Paper]
- Encode Once and Decode in Parallel: Efficient Transformer Decoding, arXiv, 2024 [Paper]
- DeFT: Decoding with Flash Tree-attention for Efficient Tree-structured LLM Inference, arXiv, 2024 [Paper]
- LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding, arXiv, 2024 [Paper]
- TriForce: Lossless Acceleration of Long Sequence Generation with Hierarchical Speculative Decoding, arXiv, 2024 [Paper]
- REST: Retrieval-Based Speculative Decoding, arXiv, 2024 [Paper]
- Tandem Transformers for Inference Efficient LLMs, arXiv, 2024 [Paper]
- PaSS: Parallel Speculative Sampling, NeurIPS Workshop, 2023 [Paper]
- Accelerating Transformer Inference for Translation via Parallel Decoding, ACL, 2023 [Paper] [Code]
- Medusa: Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads, Blog, 2023 [Blog] [Code]
- Fast Inference from Transformers via Speculative Decoding, ICML, 2023 [Paper]
- Accelerating LLM Inference with Staged Speculative Decoding, ICML Workshop, 2023 [Paper]
- Accelerating Large Language Model Decoding with Speculative Sampling, arXiv, 2023 [Paper]
- Speculative Decoding with Big Little Decoder, NeurIPS, 2023 [Paper] [Code]
- SpecInfer: Accelerating Generative LLM Serving with Speculative Inference and Token Tree Verification, arXiv, 2023 [Paper] [Code]
- Inference with Reference: Lossless Acceleration of Large Language Models, arXiv, 2023 [Paper] [Code]
- SEED: Accelerating Reasoning Tree Construction via Scheduled Speculative Decoding, arXiv, 2024 [Paper]
- Palu: Compressing KV-Cache with Low-Rank Projection, arXiv, 2024 [Paper] [Code]
- LOOK-M: Look-Once Optimization in KV Cache for Efficient Multimodal Long-Context Inference, arXiv, 2024 [Paper]
- D2O: Dynamic Discriminative Operations for Efficient Generative Inference of Large Language Models, arXiv, 2024 [Paper]
- QUEST: Query-Aware Sparsity for Efficient Long-Context LLM Inference, ICML, 2024 [Paper]
- Reducing Transformer Key-Value Cache Size with Cross-Layer Attention, arXiv, 2024 [Paper]
- SnapKV : LLM Knows What You are Looking for Before Generation, arXiv, 2024 [Paper]
- Anchor-based Large Language Models, arXiv, 2024 [Paper]
- kvquant: Towards 10 million context length llm inference with kv cache quantization, arXiv, 2024 [Paper]
- GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLM, arXiv, 2024 [Paper]
- Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference, arXiv, 2024 [Paper]
- No Token Left Behind: Reliable KV Cache Compression via Importance-Aware Mixed Precision Quantization, arXiv, 2024 [Paper]
- Get More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference, arXiv, 2024 [Paper]
- WKVQuant: Quantizing Weight and Key/Value Cache for Large Language Models Gains More, arXiv, 2024 [Paper]
- On the Efficacy of Eviction Policy for Key-Value Constrained Generative Language Model Inference, arXiv, 2024 [Paper]
- KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache, arXiv, 2024 [Paper] [Code]
- Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs, ICLR, 2024 [Paper]
- SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference, arXiv, 2023 [Paper]
- H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models, NeurIPS, 2023 [Paper]
- Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time, NeurIPS, 2023 [Paper]
- Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers, arXiv, 2023 [Paper]
- LoMA: Lossless Compressed Memory Attention, arXiv, 2024 [Paper]
- MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases, arXiv, 2024 [Paper]
- GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints, EMNLP, 2023 [Paper]
- Fast Transformer Decoding: One Write-Head is All You Need, arXiv, 2019 [Paper]
- NystrΓΆmformer: A nystrΓΆm-based algorithm for approximating self-attention, AAAI, 2021 [Paper] [Code]
- Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing, NeurIPS, 2020 [Paper] [Code]
- Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks, ICML, 2019 [Paper]
- Loki: Low-Rank Keys for Efficient Sparse Attention, ICML Workshop, 2023 [Paper]
- Sumformer: Universal Approximation for Efficient Transformers, ICML Workshop, 2023 [Paper]
- FLuRKA: Fast fused Low-Rank & Kernel Attention, arXiv, 2023 [Paper]
- Scatterbrain: Unifying Sparse and Low-rank Attention, NeurlPS, 2021 [Paper] [Code]
- Rethinking Attention with Performers, ICLR, 2021 [Paper] [Code]
- Random Feature Attention, ICLR, 2021 [Paper]
- Linformer: Self-Attention with Linear Complexity, arXiv, 2020 [Paper] [Code]
- Lightweight and Efficient End-to-End Speech Recognition Using Low-Rank Transformer, ICASSP, 2020 [Paper]
- Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention, ICML, 2020 [Paper] [Code]
- Simple linear attention language models balance the recall-throughput tradeoff, arXiv, 2024 [Paper]
- Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models, arXiv, 2024 [Paper] [Code]
- Faster Causal Attention Over Large Sequences Through Sparse Flash Attention, ICML Workshop, 2023 [Paper]
- Poolingformer: Long Document Modeling with Pooling Attention, ICML, 2021 [Paper]
- Big Bird: Transformers for Longer Sequences, NeurIPS, 2020 [Paper] [Code]
- Longformer: The Long-Document Transformer, arXiv, 2020 [Paper] [Code]
- Blockwise Self-Attention for Long Document Understanding, EMNLP, 2020 [Paper] [Code]
- Generating Long Sequences with Sparse Transformers, arXiv, 2019 [Paper]
- MoA: Mixture of Sparse Attention for Automatic Large Language Model Compression, arXiv, 2024 [Paper]
- HyperAttention: Long-context Attention in Near-Linear Time, arXiv, 2023 [Paper] [Code]
- ClusterFormer: Neural Clustering Attention for Efficient and Effective Transformer, ACL, 2022 [Paper]
- Reformer: The Efficient Transformer, ICLR, 2022 [Paper] [Code]
- Sparse Sinkhorn Attention, ICML, 2020 [Paper]
- Fast Transformers with Clustered Attention, NeurIPS, 2020 [Paper] [Code]
- Efficient Content-Based Sparse Attention with Routing Transformers, TACL, 2020 [Paper] [Code]
- Self-MoE: Towards Compositional Large Language Models with Self-Specialized Experts, arXiv, 2024 [Paper]
- Lory: Fully Differentiable Mixture-of-Experts for Autoregressive Language Model Pre-training, 2024 [Paper]
- JetMoE: Reaching Llama2 Performance with 0.1M Dollars, 2024 [Paper]
- An Expert is Worth One Token: Synergizing Multiple Expert LLMs as Generalist via Expert Token Routing, 2024 [Paper]
- Mixture-of-Depths: Dynamically allocating compute in transformer-based language models, 2024 [Paper]
- Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM, 2024 [Paper]
- Mixtral of Experts, arXiv, 2024 [Paper] [Code]
- Mistral 7B, arXiv, 2023 [Paper] [Code]
- PanGu-Ξ£: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing, arXiv, 2023 [Paper]
- Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity, JMLR, 2022 [Paper] [Code]
- Efficient Large Scale Language Modeling with Mixtures of Experts, EMNLP, 2022 [Paper] [Code]
- BASE Layers: Simplifying Training of Large, Sparse Models, ICML, 2021 [Paper] [Code]
- GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding, ICLR, 2021 [Paper]
- SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts, arXiv, 2024/ins> [Paper]
- Scaling Laws for Fine-Grained Mixture of Experts, arXiv, 2024/ins> [Paper]
- Lifelong Language Pretraining with Distribution-Specialized Experts, ICML, 2023 [Paper]
- Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models, arXiv, 2023 [Paper]
- Mixture-of-Experts with Expert Choice Routing, NeurIPS, 2022 [Paper]
- StableMoE: Stable Routing Strategy for Mixture of Experts, ACL, 2022 [Paper] [Code]
- On the Representation Collapse of Sparse Mixture of Experts, NeurIPS, 2022 [Paper]
- Two Stones Hit One Bird: Bilevel Positional Encoding for Better Length Extrapolation, ICML, 2024 [Paper]
- β-Bench: Extending Long Context Evaluation Beyond 100K Tokens, arXiv, 2024 [Paper]
- Resonance RoPE: Improving Context Length Generalization of Large Language Models, arXiv, 2024 [Paper] [Code]
- LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens, arXiv, 2024 [Paper]
- E^2-LLM:Efficient and Extreme Length Extension of Large Language Models, arXiv, 2024 [Paper]
- Scaling Laws of RoPE-based Extrapolation, arXiv, 2023 [Paper]
- A Length-Extrapolatable Transformer, ACL, 2023 [Paper] [Code]
- Extending Context Window of Large Language Models via Positional Interpolation, arXiv, 2023 [Paper]
- NTK Interpolation, Blog, 2023 [Reddit post]
- YaRN: Efficient Context Window Extension of Large Language Models, arXiv, 2023 [Paper] [Code]
- CLEX: Continuous Length Extrapolation for Large Language Models, arXiv, 2023 [Paper][Code]
- PoSE: Efficient Context Window Extension of LLMs via Positional Skip-wise Training, arXiv, 2023 [Paper][Code]
- Functional Interpolation for Relative Positions Improves Long Context Transformers, arXiv, 2023 [Paper]
- Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation, ICLR, 2022 [Paper] [Code]
- Exploring Length Generalization in Large Language Models, NeurIPS, 2022 [Paper]
- Retentive Network: A Successor to Transformer for Large Language Models, arXiv, 2023 [Paper] [Code]
- Recurrent Memory Transformer, NeurIPS, 2022 [Paper] [Code]
- Block-Recurrent Transformers, NeurIPS, 2022 [Paper] [Code]
- β-former: Infinite Memory Transformer, ACL, 2022 [Paper] [Code]
- Memformer: A Memory-Augmented Transformer for Sequence Modeling, AACL-Findings, 2020 [Paper] [Code]
- Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context, ACL, 2019 [Paper] [Code]
- XL3M: A Training-free Framework for LLM Length Extension Based on Segment-wise Inference, arXiv, 2024 [Paper]
- TransformerFAM: Feedback attention is working memory, arXiv, 2024 [Paper]
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Efficient Training | Efficient Inference | Efficient Fine-Tuning | |
---|---|---|---|
DeepSpeed [Code] | β | β | β |
Megatron [Code] | β | β | β |
ColossalAI [Code] | β | β | β |
Nanotron [Code] | β | β | β |
MegaBlocks [Code] | β | β | β |
FairScale [Code] | β | β | β |
Pax [Code] | β | β | β |
Composer [Code] | β | β | β |
OpenLLM [Code] | β | β | β |
LLM-Foundry [Code] | β | β | β |
vLLM [Code] | β | β | β |
TensorRT-LLM [Code] | β | β | β |
TGI [Code] | β | β | β |
RayLLM [Code] | β | β | β |
MLC LLM [Code] | β | β | β |
Sax [Code] | β | β | β |
Mosec [Code] | β | β | β |