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Rho-1: Not All Tokens Are What You Need
Paper • 2404.07965 • Published • 88 -
VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time
Paper • 2404.10667 • Published • 18 -
Instruction-tuned Language Models are Better Knowledge Learners
Paper • 2402.12847 • Published • 26 -
DoRA: Weight-Decomposed Low-Rank Adaptation
Paper • 2402.09353 • Published • 26
Collections
Discover the best community collections!
Collections including paper arxiv:2403.19887
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Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
Paper • 2404.02258 • Published • 104 -
Textbooks Are All You Need
Paper • 2306.11644 • Published • 143 -
Jamba: A Hybrid Transformer-Mamba Language Model
Paper • 2403.19887 • Published • 106 -
Large Language Models Struggle to Learn Long-Tail Knowledge
Paper • 2211.08411 • Published • 3
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Lost in the Middle: How Language Models Use Long Contexts
Paper • 2307.03172 • Published • 37 -
Efficient Estimation of Word Representations in Vector Space
Paper • 1301.3781 • Published • 6 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 16 -
Attention Is All You Need
Paper • 1706.03762 • Published • 50
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Large Language Model Unlearning via Embedding-Corrupted Prompts
Paper • 2406.07933 • Published • 7 -
Block Transformer: Global-to-Local Language Modeling for Fast Inference
Paper • 2406.02657 • Published • 38 -
Learn Beyond The Answer: Training Language Models with Reflection for Mathematical Reasoning
Paper • 2406.12050 • Published • 19 -
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Paper • 2406.11813 • Published • 31
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ReAct: Synergizing Reasoning and Acting in Language Models
Paper • 2210.03629 • Published • 15 -
Attention Is All You Need
Paper • 1706.03762 • Published • 50 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 16 -
Jamba: A Hybrid Transformer-Mamba Language Model
Paper • 2403.19887 • Published • 106
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MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases
Paper • 2402.14905 • Published • 127 -
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 607 -
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
Paper • 2403.09611 • Published • 125 -
Jamba: A Hybrid Transformer-Mamba Language Model
Paper • 2403.19887 • Published • 106
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Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length
Paper • 2404.08801 • Published • 65 -
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
Paper • 2404.07839 • Published • 43 -
Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence
Paper • 2404.05892 • Published • 33 -
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Paper • 2312.00752 • Published • 139
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OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 82 -
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Paper • 2403.05530 • Published • 62 -
StarCoder: may the source be with you!
Paper • 2305.06161 • Published • 30 -
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
Paper • 2312.15166 • Published • 56
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Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
Paper • 2404.02258 • Published • 104 -
Jamba: A Hybrid Transformer-Mamba Language Model
Paper • 2403.19887 • Published • 106 -
EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba
Paper • 2403.09977 • Published • 10 -
SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series
Paper • 2403.15360 • Published • 12