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Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
Paper • 2403.14608 • Published -
Towards Better Parameter-Efficient Fine-Tuning for Large Language Models: A Position Paper
Paper • 2311.13126 • Published • 1 -
Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models
Paper • 2409.09510 • Published -
Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuning
Paper • 2407.01320 • Published
Collections
Discover the best community collections!
Collections including paper arxiv:2303.15647
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OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework
Paper • 2404.14619 • Published • 126 -
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
Paper • 2303.15647 • Published • 4 -
Hyper-X: A Unified Hypernetwork for Multi-Task Multilingual Transfer
Paper • 2205.12148 • Published • 2 -
No More Adam: Learning Rate Scaling at Initialization is All You Need
Paper • 2412.11768 • Published • 43
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Attention Is All You Need
Paper • 1706.03762 • Published • 120 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 26 -
Universal Language Model Fine-tuning for Text Classification
Paper • 1801.06146 • Published • 8 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 20
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LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
Paper • 2310.08659 • Published • 29 -
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Paper • 2309.14717 • Published • 46 -
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
Paper • 2309.16119 • Published • 1 -
LoRA ensembles for large language model fine-tuning
Paper • 2310.00035 • Published • 2
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On the Scalability of Diffusion-based Text-to-Image Generation
Paper • 2404.02883 • Published • 19 -
MonoPatchNeRF: Improving Neural Radiance Fields with Patch-based Monocular Guidance
Paper • 2404.08252 • Published • 6 -
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
Paper • 2303.15647 • Published • 4
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Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning
Paper • 2310.20587 • Published • 18 -
SELF: Language-Driven Self-Evolution for Large Language Model
Paper • 2310.00533 • Published • 2 -
QLoRA: Efficient Finetuning of Quantized LLMs
Paper • 2305.14314 • Published • 61 -
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Paper • 2309.14717 • Published • 46
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LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery
Paper • 2310.18356 • Published • 24 -
LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
Paper • 2310.08659 • Published • 29 -
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
Paper • 2309.16119 • Published • 1 -
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Paper • 2309.14717 • Published • 46
-
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
Paper • 2403.14608 • Published -
Towards Better Parameter-Efficient Fine-Tuning for Large Language Models: A Position Paper
Paper • 2311.13126 • Published • 1 -
Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models
Paper • 2409.09510 • Published -
Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuning
Paper • 2407.01320 • Published
-
OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework
Paper • 2404.14619 • Published • 126 -
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
Paper • 2303.15647 • Published • 4 -
Hyper-X: A Unified Hypernetwork for Multi-Task Multilingual Transfer
Paper • 2205.12148 • Published • 2 -
No More Adam: Learning Rate Scaling at Initialization is All You Need
Paper • 2412.11768 • Published • 43
-
On the Scalability of Diffusion-based Text-to-Image Generation
Paper • 2404.02883 • Published • 19 -
MonoPatchNeRF: Improving Neural Radiance Fields with Patch-based Monocular Guidance
Paper • 2404.08252 • Published • 6 -
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
Paper • 2303.15647 • Published • 4
-
Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning
Paper • 2310.20587 • Published • 18 -
SELF: Language-Driven Self-Evolution for Large Language Model
Paper • 2310.00533 • Published • 2 -
QLoRA: Efficient Finetuning of Quantized LLMs
Paper • 2305.14314 • Published • 61 -
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Paper • 2309.14717 • Published • 46
-
Attention Is All You Need
Paper • 1706.03762 • Published • 120 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 26 -
Universal Language Model Fine-tuning for Text Classification
Paper • 1801.06146 • Published • 8 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 20
-
LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery
Paper • 2310.18356 • Published • 24 -
LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
Paper • 2310.08659 • Published • 29 -
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
Paper • 2309.16119 • Published • 1 -
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Paper • 2309.14717 • Published • 46
-
LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
Paper • 2310.08659 • Published • 29 -
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Paper • 2309.14717 • Published • 46 -
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
Paper • 2309.16119 • Published • 1 -
LoRA ensembles for large language model fine-tuning
Paper • 2310.00035 • Published • 2