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XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference
Paper • 2404.15420 • Published • 10 -
OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework
Paper • 2404.14619 • Published • 127 -
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Paper • 2404.14219 • Published • 256 -
How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study
Paper • 2404.14047 • Published • 45
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Collections including paper arxiv:2404.14047
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Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Paper • 2404.14219 • Published • 256 -
How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study
Paper • 2404.14047 • Published • 45 -
Octopus v4: Graph of language models
Paper • 2404.19296 • Published • 117 -
DeepSeek-V3 Technical Report
Paper • 2412.19437 • Published • 55
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Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Paper • 2404.14219 • Published • 256 -
How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study
Paper • 2404.14047 • Published • 45 -
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 610
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How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study
Paper • 2404.14047 • Published • 45 -
LiteSearch: Efficacious Tree Search for LLM
Paper • 2407.00320 • Published • 39 -
Cut Your Losses in Large-Vocabulary Language Models
Paper • 2411.09009 • Published • 47 -
LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models
Paper • 2411.09595 • Published • 73
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How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study
Paper • 2404.14047 • Published • 45 -
Reasoning in Large Language Models: A Geometric Perspective
Paper • 2407.02678 • Published • 1 -
Natural Language Reinforcement Learning
Paper • 2411.14251 • Published • 29 -
Byte Latent Transformer: Patches Scale Better Than Tokens
Paper • 2412.09871 • Published • 93
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The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 610 -
BitNet: Scaling 1-bit Transformers for Large Language Models
Paper • 2310.11453 • Published • 97 -
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
Paper • 2404.02258 • Published • 104 -
TransformerFAM: Feedback attention is working memory
Paper • 2404.09173 • Published • 43