Awesome-Efficient-AIGC
A list of papers, docs, codes about efficient AIGC. This repo is aimed to provide the info for efficient AIGC research, including language and vision, we are continuously improving the project. Welcome to PR the works (papers, repositories) that are missed by the repo.
https://github.com/Efficient-ML/Awesome-Efficient-AIGC
Last synced: 9 days ago
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Language
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2023
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
- [Nature - efficient fine-tuning of large-scale pre-trained language models [[code](https://github.com/thunlp/OpenDelta)]
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2024
- [arXiv - bit Quantized LLaMA3 Models? An Empirical Study [[code](https://github.com/Macaronlin/LLaMA3-Quantization)] [[HuggingFace](https://huggingface.co/LLMQ)]
- [ArXiv - Finetuning Quantization of LLMs via Information Retention [[code](https://github.com/htqin/IR-QLoRA)]
- [ArXiv - Training Quantization for LLMs [[code](https://github.com/Aaronhuang-778/BiLLM)]
- [ArXiv - LLM: Accurate Dual-Binarization for Efficient LLMs
- [ArXiv
- [ArXiv - Efficient Tuning of Quantized Large Language Models
- [ArXiv - LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design
- [ArXiv
- [ArXiv - Aware Training for the Acceleration of Lightweight LLMs on the Edge [[code](https://github.com/shawnricecake/EdgeQAT)] 
- [ArXiv - Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs
- [ArXiv - Rank Quantization Error Reconstruction for LLMs
- [ArXiv - Free Asymmetric 2bit Quantization for KV Cache [[code](https://github.com/jy-yuan/KIVI)] 
- [ArXiv - RelaxML/quip-sharp)] 
- [ArXiv - Aware Training on Large Language Models via LoRA-wise LSQ
- [ArXiv - Aware Dequantization
- [ArXiv - Bit Quantized Large Language Model
- [ArXiv - 4-Bit LLMs via Self-Distillation [[code](https://github.com/DD-DuDa/BitDistiller)] 
- [ArXiv - bit Large Language Models
- [ArXiv
- [ArXiv - ai-research/gptvq)] 
- [DAC - aware Post-Training Mixed-Precision Quantization for Large Language Models
- [DAC
- [ArXiv - Aware Mixed Precision Quantization
- [ArXiv
- [ArXiv - bound for Large Language Models with Per-tensor Quantization
- [ArXiv - PQ: Serving LLM on Heterogeneous Clusters with Phase-Aware Partition and Adaptive Quantization
- [ArXiv
- [ArXiv
- [ArXiv - free Quantization Algorithm for LLMs
- [ArXiv - KVCacheQuantization)] 
- [ArXiv - Lossless Generative Inference of LLM
- [ArXiv
- [ArXiv - LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression [[code](https://github.com/AIoT-MLSys-Lab/SVD-LLM)] 
- [ICLR
- [ICLR Practical ML for Low Resource Settings Workshop
- [ArXiv
- [ArXiv - Free 4-Bit Inference in Rotated LLMs [[code](https://github.com/spcl/QuaRot)] 
- [ArXiv - compensation)] 
- [ArXiv
- [ArXiv - Tune May Only Be Worth One Bit [[code](https://github.com/FasterDecoding/BitDelta)] 
- [AAAI EIW Workshop 2024 - Rank Adaptation for Efficient Large Language Model Tuning
- [ArXiv
- [ArXiv
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Star History
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2023
- ![Star History Chart - history.com/#Efficient-ML/Awesome-Efficient-LLM-Diffusion&Timeline)
- ![Star History Chart - history.com/#Efficient-ML/Awesome-Efficient-LLM-Diffusion&Timeline)
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Survey
- [Arxiv
- [Arxiv - LLM-Survey)] 
- [Arxiv - Knowledge-Distillation-of-LLMs)] 
- [Arxiv
- [Arxiv
- [Arxiv
- [Arxiv - Bench)]  [[Blog]](https://sites.google.com/view/spec-bench)
- [Arxiv
- [Arxiv
- [Arxiv - efficient LLM and Multimodal Foundation Models [[code](https://github.com/UbiquitousLearning/Efficient_Foundation_Model_Survey)] 
- [Arxiv - Efficient Large Language Models [[code](https://github.com/tiingweii-shii/Awesome-Resource-Efficient-LLM-Papers)] 
- [Arxiv
- [Arxiv - MLSys-Lab/Efficient-LLMs-Survey)] 
- [Arxiv - LLM-Survey)] 
- [Arxiv
- [Arxiv
- [TACL - Scale Transformer-Based Models: A Case Study on BERT
- [Arxiv
- [JSA
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Vision
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2023
- [ICLR - Conditioning
- [CVPR - training Quantization on Diffusion Models [[code](https://github.com/42Shawn/PTQ4DM)] 
- [CVPR
- [ICCV - Diffusion: Quantizing Diffusion Models [[code](https://github.com/Xiuyu-Li/q-diffusion)] 
- [NeurIPS - DM: An Efficient Low-bit Quantized Diffusion Model
- [NeurIPS - Training Quantization for Diffusion Models [[code](https://github.com/ziplab/PTQD)] 
- [NeurIPS
- [ArXiv - free Quantization for Diffusion Models
- [ArXiv - VAE Made Simple [[code](https://github.com/google-research/google-research/tree/master/fsq)] 
- [ArXiv - Aware Fine-Tuning of Low-Bit Diffusion Models
- [TPAMI
- [ArXiv - Pruning)]
- [CVPR
- [ICME - based Feature Distillation [[code](https://github.com/zju-SWJ/RCFD)]
- [ICML - based Combinatorial Optimization Solvers by Progressive Distillation [[code](https://github.com/jwrh/Accelerating-Diffusion-based-Combinatorial-Optimization-Solvers-by-Progressive-Distillation)]
- [ICML - Distillation of Internet-Scale Text-to-Image Diffusion Models [[code](https://github.com/nannullna/safe-diffusion)]
- [ArXiv - free Distillation of Denoising Diffusion Models with Bootstrapping
- [ArXiv - to-Image Diffusion Model on Mobile Devices within Two Seconds
- [ArXiv - Up Distillation: You Only Need to Train Once for Accelerating Sampling [[code](https://anonymous.4open.science/r/Catch-Up-Distillation-E31F)]
- [ArXiv
- [ArXiv - to-Image Diffusion Models
- [ArXiv
- [ArXiv - Free Optimization of Time Steps and Architectures for Automated Diffusion Model Acceleration
- [ArXiv - Diffusion: Vector Quantized Discrete Diffusion Model with Spiking Neural Networks [[code](https://github.com/Arktis2022/Spiking-Diffusion)]
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2024
- [ArXiv - Zheng/BinaryDM)]
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Programming Languages
Categories