BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
Paper
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2402.04291
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Published
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48
quantization
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OneBit: Towards Extremely Low-bit Large Language Models
Paper • 2402.11295 • Published • 23 -
A Survey on Transformer Compression
Paper • 2402.05964 • Published -
Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers
Paper • 2402.08958 • Published • 3 -
BitDelta: Your Fine-Tune May Only Be Worth One Bit
Paper • 2402.10193 • Published • 19 -
GPTVQ: The Blessing of Dimensionality for LLM Quantization
Paper • 2402.15319 • Published • 19 -
EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs
Paper • 2403.02775 • Published • 11 -
4-bit Shampoo for Memory-Efficient Network Training
Paper • 2405.18144 • Published • 9 -
PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers in LLMs
Paper • 2410.05265 • Published • 30 -
BitNet a4.8: 4-bit Activations for 1-bit LLMs
Paper • 2411.04965 • Published • 64 -
"Give Me BF16 or Give Me Death"? Accuracy-Performance Trade-Offs in LLM Quantization
Paper • 2411.02355 • Published • 46 -
NeuZip: Memory-Efficient Training and Inference with Dynamic Compression of Neural Networks
Paper • 2410.20650 • Published • 16 -
BitStack: Fine-Grained Size Control for Compressed Large Language Models in Variable Memory Environments
Paper • 2410.23918 • Published • 18