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Scaling Instruction-Finetuned Language Models
Paper • 2210.11416 • Published • 7 -
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Paper • 2312.00752 • Published • 138 -
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Paper • 2403.05530 • Published • 60 -
Yi: Open Foundation Models by 01.AI
Paper • 2403.04652 • Published • 62
Collections
Discover the best community collections!
Collections including paper arxiv:2402.18668
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StarCoder 2 and The Stack v2: The Next Generation
Paper • 2402.19173 • Published • 134 -
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Paper • 2402.19427 • Published • 52 -
Simple linear attention language models balance the recall-throughput tradeoff
Paper • 2402.18668 • Published • 18 -
Priority Sampling of Large Language Models for Compilers
Paper • 2402.18734 • Published • 16
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Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Paper • 2402.19427 • Published • 52 -
Beyond Language Models: Byte Models are Digital World Simulators
Paper • 2402.19155 • Published • 49 -
StarCoder 2 and The Stack v2: The Next Generation
Paper • 2402.19173 • Published • 134 -
Simple linear attention language models balance the recall-throughput tradeoff
Paper • 2402.18668 • Published • 18
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MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs
Paper • 2402.15627 • Published • 34 -
Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models
Paper • 2402.17177 • Published • 88 -
Beyond Language Models: Byte Models are Digital World Simulators
Paper • 2402.19155 • Published • 49 -
Hydragen: High-Throughput LLM Inference with Shared Prefixes
Paper • 2402.05099 • Published • 18
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LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens
Paper • 2402.13753 • Published • 111 -
Data Engineering for Scaling Language Models to 128K Context
Paper • 2402.10171 • Published • 21 -
LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration
Paper • 2402.11550 • Published • 15 -
The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey
Paper • 2401.07872 • Published • 2
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Simple linear attention language models balance the recall-throughput tradeoff
Paper • 2402.18668 • Published • 18 -
Linear Transformers with Learnable Kernel Functions are Better In-Context Models
Paper • 2402.10644 • Published • 78 -
Repeat After Me: Transformers are Better than State Space Models at Copying
Paper • 2402.01032 • Published • 22 -
Zoology: Measuring and Improving Recall in Efficient Language Models
Paper • 2312.04927 • Published • 2
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Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Paper • 2402.19427 • Published • 52 -
Simple linear attention language models balance the recall-throughput tradeoff
Paper • 2402.18668 • Published • 18 -
ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition
Paper • 2402.15220 • Published • 19 -
Linear Transformers are Versatile In-Context Learners
Paper • 2402.14180 • Published • 6