Addition is All You Need for Energy-efficient Language Models
Paper
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2410.00907
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Published
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151
Ternary LLMs & Knowledge distillation & SOTA
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The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 615 -
LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding
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Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory
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Token-Scaled Logit Distillation for Ternary Weight Generative Language Models
Paper • 2308.06744 • Published • 1 -
TerDiT: Ternary Diffusion Models with Transformers
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Reducing Transformer Key-Value Cache Size with Cross-Layer Attention
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You Only Cache Once: Decoder-Decoder Architectures for Language Models
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Differential Transformer
Paper • 2410.05258 • Published • 178 -
BitNet a4.8: 4-bit Activations for 1-bit LLMs
Paper • 2411.04965 • Published • 69 -
Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention
Paper • 2502.11089 • Published • 156