LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
Paper • 2310.08659 • Published • 28
Quantization
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QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Paper • 2309.14717 • Published • 46 -
Norm Tweaking: High-performance Low-bit Quantization of Large Language Models
Paper • 2309.02784 • Published • 2 -
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
Paper • 2309.16119 • Published • 1 -
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
Paper • 2308.13137 • Published • 19 -
FPTQ: Fine-grained Post-Training Quantization for Large Language Models
Paper • 2308.15987 • Published • 2 -
QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models
Paper • 2310.16795 • Published • 27 -
LLM-FP4: 4-Bit Floating-Point Quantized Transformers
Paper • 2310.16836 • Published • 14 -
Microscaling Data Formats for Deep Learning
Paper • 2310.10537 • Published • 8 -
DeepliteRT: Computer Vision at the Edge
Paper • 2309.10878 • Published • 2 -
Efficient Post-training Quantization with FP8 Formats
Paper • 2309.14592 • Published • 11 -
NUPES : Non-Uniform Post-Training Quantization via Power Exponent Search
Paper • 2308.05600 • Published • 1 -
BitNet: Scaling 1-bit Transformers for Large Language Models
Paper • 2310.11453 • Published • 106 -
Understanding the Impact of Post-Training Quantization on Large Language Models
Paper • 2309.05210 • Published • 1 -
FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only Quantization for LLMs
Paper • 2308.09723 • Published • 2 -
Softmax Bias Correction for Quantized Generative Models
Paper • 2309.01729 • Published • 1 -
Training and inference of large language models using 8-bit floating point
Paper • 2309.17224 • Published • 1 -
TEQ: Trainable Equivalent Transformation for Quantization of LLMs
Paper • 2310.10944 • Published • 10 -
QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models
Paper • 2310.08041 • Published • 1 -
Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs
Paper • 2309.05516 • Published • 11 -
PB-LLM: Partially Binarized Large Language Models
Paper • 2310.00034 • Published • 2 -
Towards End-to-end 4-Bit Inference on Generative Large Language Models
Paper • 2310.09259 • Published • 1 -
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt
Paper • 2305.11186 • Published • 1 -
MEMORY-VQ: Compression for Tractable Internet-Scale Memory
Paper • 2308.14903 • Published • 1 -
FP8-LM: Training FP8 Large Language Models
Paper • 2310.18313 • Published • 33 -
Atom: Low-bit Quantization for Efficient and Accurate LLM Serving
Paper • 2310.19102 • Published • 11 -
QLoRA: Efficient Finetuning of Quantized LLMs
Paper • 2305.14314 • Published • 59 -
A Survey on Model Compression for Large Language Models
Paper • 2308.07633 • Published • 3 -
REx: Data-Free Residual Quantization Error Expansion
Paper • 2203.14645 • Published • 1 -
Data-Free Quantization with Accurate Activation Clipping and Adaptive Batch Normalization
Paper • 2204.04215 • Published • 2 -
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
Paper • 2305.17888 • Published • 1 -
Token-Scaled Logit Distillation for Ternary Weight Generative Language Models
Paper • 2308.06744 • Published • 1 -
Understanding and Improving Knowledge Distillation for Quantization-Aware Training of Large Transformer Encoders
Paper • 2211.11014 • Published • 1 -
Quantized Feature Distillation for Network Quantization
Paper • 2307.10638 • Published • 1 -
Model compression via distillation and quantization
Paper • 1802.05668 • Published • 1 -
Adaptive Precision Training (AdaPT): A dynamic fixed point quantized training approach for DNNs
Paper • 2107.13490 • Published • 1 -
Feature Affinity Assisted Knowledge Distillation and Quantization of Deep Neural Networks on Label-Free Data
Paper • 2302.10899 • Published • 1 -
Compressing LLMs: The Truth is Rarely Pure and Never Simple
Paper • 2310.01382 • Published • 1 -
Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing
Paper • 2306.12929 • Published • 13 -
Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling
Paper • 2304.09145 • Published • 1 -
LORD: Low Rank Decomposition Of Monolingual Code LLMs For One-Shot Compression
Paper • 2309.14021 • Published • 1 -
Prune Once for All: Sparse Pre-Trained Language Models
Paper • 2111.05754 • Published • 2 -
eDKM: An Efficient and Accurate Train-time Weight Clustering for Large Language Models
Paper • 2309.00964 • Published • 3 -
Mixture of Quantized Experts (MoQE): Complementary Effect of Low-bit Quantization and Robustness
Paper • 2310.02410 • Published • 3 -
SlimFit: Memory-Efficient Fine-Tuning of Transformer-based Models Using Training Dynamics
Paper • 2305.18513 • Published • 2 -
NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers
Paper • 2211.16056 • Published • 4 -
LQ-LoRA: Low-rank Plus Quantized Matrix Decomposition for Efficient Language Model Finetuning
Paper • 2311.12023 • Published • 2 -
Blockwise Compression of Transformer-based Models without Retraining
Paper • 2304.01483 • Published • 1 -
Towards Fine-tuning Pre-trained Language Models with Integer Forward and Backward Propagation
Paper • 2209.09815 • Published • 1 -
Learning Low-Rank Representations for Model Compression
Paper • 2211.11397 • Published • 1 -
Ada-QPacknet -- adaptive pruning with bit width reduction as an efficient continual learning method without forgetting
Paper • 2308.07939 • Published • 1 -
Efficient Storage of Fine-Tuned Models via Low-Rank Approximation of Weight Residuals
Paper • 2305.18425 • Published • 1 -
BitDelta: Your Fine-Tune May Only Be Worth One Bit
Paper • 2402.10193 • Published • 21