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Attention Is All You Need
Paper • 1706.03762 • Published • 36 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 11 -
Universal Language Model Fine-tuning for Text Classification
Paper • 1801.06146 • Published • 6 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 10
Collections
Discover the best community collections!
Collections including paper arxiv:2106.09685
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LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery
Paper • 2310.18356 • Published • 22 -
LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
Paper • 2310.08659 • Published • 20 -
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 • 43
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LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
Paper • 2310.08659 • Published • 20 -
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Paper • 2309.14717 • Published • 43 -
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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Attention Is All You Need
Paper • 1706.03762 • Published • 36 -
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Paper • 2005.11401 • Published • 11 -
LoRA: Low-Rank Adaptation of Large Language Models
Paper • 2106.09685 • Published • 24 -
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Paper • 2205.14135 • Published • 8
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DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models
Paper • 2309.03883 • Published • 14 -
LoRA: Low-Rank Adaptation of Large Language Models
Paper • 2106.09685 • Published • 24 -
Agents: An Open-source Framework for Autonomous Language Agents
Paper • 2309.07870 • Published • 39 -
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
Paper • 2309.00267 • Published • 45