QuIP: 2-Bit Quantization of Large Language Models With Guarantees
Paper
•
2307.13304
•
Published
•
2
quantize
-
-
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression
Paper • 2306.03078 • Published • 3 -
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
Paper • 2308.13137 • Published • 17 -
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Paper • 2306.00978 • Published • 8 -
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
Paper • 2210.17323 • Published • 8 -
ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks
Paper • 2312.08583 • Published • 9 -
QLoRA: Efficient Finetuning of Quantized LLMs
Paper • 2305.14314 • Published • 46 -
LLM-FP4: 4-Bit Floating-Point Quantized Transformers
Paper • 2310.16836 • Published • 13 -
FP8-LM: Training FP8 Large Language Models
Paper • 2310.18313 • Published • 31 -
FP8 Quantization: The Power of the Exponent
Paper • 2208.09225 • Published -
Atom: Low-bit Quantization for Efficient and Accurate LLM Serving
Paper • 2310.19102 • Published • 10 -
QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models
Paper • 2310.08041 • Published • 1 -
Towards End-to-end 4-Bit Inference on Generative Large Language Models
Paper • 2310.09259 • Published • 1 -
Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling
Paper • 2304.09145 • Published • 1 -
Dual Grained Quantization: Efficient Fine-Grained Quantization for LLM
Paper • 2310.04836 • Published • 1 -
Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models
Paper • 2309.15531 • Published • 1 -
LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
Paper • 2310.08659 • Published • 22 -
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Paper • 2309.14717 • Published • 44 -
Norm Tweaking: High-performance Low-bit Quantization of Large Language Models
Paper • 2309.02784 • Published • 1 -
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
Paper • 2309.16119 • Published • 1 -
Training and inference of large language models using 8-bit floating point
Paper • 2309.17224 • Published • 1 -
BitNet: Scaling 1-bit Transformers for Large Language Models
Paper • 2310.11453 • Published • 96 -
Understanding the Impact of Post-Training Quantization on Large Language Models
Paper • 2309.05210 • Published • 1 -
PB-LLM: Partially Binarized Large Language Models
Paper • 2310.00034 • Published • 1 -
TEQ: Trainable Equivalent Transformation for Quantization of LLMs
Paper • 2310.10944 • Published • 9 -
QFT: Quantized Full-parameter Tuning of LLMs with Affordable Resources
Paper • 2310.07147 • Published • 1 -
FPTQ: Fine-grained Post-Training Quantization for Large Language Models
Paper • 2308.15987 • Published • 1 -
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
Paper • 2211.10438 • Published • 4 -
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
Paper • 2208.07339 • Published • 4 -
Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs
Paper • 2309.05516 • Published • 9 -
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
Paper • 2402.04291 • Published • 48 -
Extreme Compression of Large Language Models via Additive Quantization
Paper • 2401.06118 • Published • 12 -
KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization
Paper • 2401.18079 • Published • 7 -
GPTVQ: The Blessing of Dimensionality for LLM Quantization
Paper • 2402.15319 • Published • 19 -
The case for 4-bit precision: k-bit Inference Scaling Laws
Paper • 2212.09720 • Published • 3 -
SqueezeLLM: Dense-and-Sparse Quantization
Paper • 2306.07629 • Published • 4 -
QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks
Paper • 2402.04396 • Published • 1