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Rethinking Interpretability in the Era of Large Language Models
Paper • 2402.01761 • Published • 23 -
OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models
Paper • 2402.01739 • Published • 28 -
BitNet: Scaling 1-bit Transformers for Large Language Models
Paper • 2310.11453 • Published • 107 -
Stealing Part of a Production Language Model
Paper • 2403.06634 • Published • 88
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Shortened LLaMA: A Simple Depth Pruning for Large Language Models
Paper • 2402.02834 • Published • 17 -
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
Paper • 2402.04291 • Published • 51 -
PB-LLM: Partially Binarized Large Language Models
Paper • 2310.00034 • Published • 2 -
ShortGPT: Layers in Large Language Models are More Redundant Than You Expect
Paper • 2403.03853 • Published • 66
-
Rethinking Interpretability in the Era of Large Language Models
Paper • 2402.01761 • Published • 23 -
OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models
Paper • 2402.01739 • Published • 28 -
BitNet: Scaling 1-bit Transformers for Large Language Models
Paper • 2310.11453 • Published • 107 -
Stealing Part of a Production Language Model
Paper • 2403.06634 • Published • 88
-
Shortened LLaMA: A Simple Depth Pruning for Large Language Models
Paper • 2402.02834 • Published • 17 -
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
Paper • 2402.04291 • Published • 51 -
PB-LLM: Partially Binarized Large Language Models
Paper • 2310.00034 • Published • 2 -
ShortGPT: Layers in Large Language Models are More Redundant Than You Expect
Paper • 2403.03853 • Published • 66