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Collections including paper arxiv:2310.16836

QuIP: 2-Bit Quantization of Large Language Models With Guarantees

Paper • 2307.13304 • Published Jul 25, 2023 • 1
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Paper • 2306.03078 • Published Jun 5, 2023 • 3
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Paper • 2308.13137 • Published Aug 25, 2023 • 14
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Paper • 2306.00978 • Published Jun 1, 2023 • 5

XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference

Paper • 2404.15420 • Published Apr 23 • 7
OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework

Paper • 2404.14619 • Published Apr 22 • 124
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Paper • 2404.14219 • Published Apr 22 • 239
How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study

Paper • 2404.14047 • Published Apr 22 • 38

LLM-FP4: 4-Bit Floating-Point Quantized Transformers

Paper • 2310.16836 • Published Oct 25, 2023 • 10
FinGPT: Large Generative Models for a Small Language

Paper • 2311.05640 • Published Nov 3, 2023 • 26
Memory Augmented Language Models through Mixture of Word Experts

Paper • 2311.10768 • Published Nov 15, 2023 • 16

Papers: Quantization

FP8-LM: Training FP8 Large Language Models

Paper • 2310.18313 • Published Oct 27, 2023 • 30
LLM-FP4: 4-Bit Floating-Point Quantized Transformers

Paper • 2310.16836 • Published Oct 25, 2023 • 10
TEQ: Trainable Equivalent Transformation for Quantization of LLMs

Paper • 2310.10944 • Published Oct 17, 2023 • 9
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers

Paper • 2309.16119 • Published Sep 28, 2023 • 1

LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models

Paper • 2310.08659 • Published Oct 12, 2023 • 20
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models

Paper • 2309.14717 • Published Sep 26, 2023 • 43
Norm Tweaking: High-performance Low-bit Quantization of Large Language Models

Paper • 2309.02784 • Published Sep 6, 2023 • 1
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers

Paper • 2309.16119 • Published Sep 28, 2023 • 1

Approximating Two-Layer Feedforward Networks for Efficient Transformers

Paper • 2310.10837 • Published Oct 16, 2023 • 10
BitNet: Scaling 1-bit Transformers for Large Language Models

Paper • 2310.11453 • Published Oct 17, 2023 • 94
QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models

Paper • 2310.16795 • Published Oct 25, 2023 • 26
LLM-FP4: 4-Bit Floating-Point Quantized Transformers

Paper • 2310.16836 • Published Oct 25, 2023 • 10

interesting stuff

Chain-of-Verification Reduces Hallucination in Large Language Models

Paper • 2309.11495 • Published Sep 20, 2023 • 37
Adapting Large Language Models via Reading Comprehension

Paper • 2309.09530 • Published Sep 18, 2023 • 73
CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages

Paper • 2309.09400 • Published Sep 17, 2023 • 77
Language Modeling Is Compression

Paper • 2309.10668 • Published Sep 19, 2023 • 81

TinyStories: How Small Can Language Models Be and Still Speak Coherent English?

Paper • 2305.07759 • Published May 12, 2023 • 29
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models

Paper • 2309.14717 • Published Sep 26, 2023 • 43
BitNet: Scaling 1-bit Transformers for Large Language Models

Paper • 2310.11453 • Published Oct 17, 2023 • 94
LLM-FP4: 4-Bit Floating-Point Quantized Transformers

Paper • 2310.16836 • Published Oct 25, 2023 • 10

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