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Collections including paper arxiv:2210.17323

EfficientQAT: Efficient Quantization-Aware Training for Large Language Models

Paper • 2407.11062 • Published Jul 10 • 8
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Paper • 2210.17323 • Published Oct 31, 2022 • 7
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Paper • 2306.00978 • Published Jun 1, 2023 • 8

Quantization Papers

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Paper • 2306.00978 • Published Jun 1, 2023 • 8
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Paper • 2210.17323 • Published Oct 31, 2022 • 7
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 602

Attention Is All You Need

Paper • 1706.03762 • Published Jun 12, 2017 • 44
LLaMA: Open and Efficient Foundation Language Models

Paper • 2302.13971 • Published Feb 27, 2023 • 13
Efficient Tool Use with Chain-of-Abstraction Reasoning

Paper • 2401.17464 • Published Jan 30 • 16
MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts

Paper • 2407.21770 • Published Jul 31 • 22

🔧 Optimization Mechanics 🔧

GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Paper • 2210.17323 • Published Oct 31, 2022 • 7
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Paper • 2208.07339 • Published Aug 15, 2022 • 4
Hydragen: High-Throughput LLM Inference with Shared Prefixes

Paper • 2402.05099 • Published Feb 7 • 18
Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads

Paper • 2401.10774 • Published Jan 19 • 53

A little guide to building Large Language Models in 2024

Resources mentioned by @thomwolf in https://x.com/Thom_Wolf/status/1773340316835131757

Yi: Open Foundation Models by 01.AI

Paper • 2403.04652 • Published Mar 7 • 62
A Survey on Data Selection for Language Models

Paper • 2402.16827 • Published Feb 26 • 4
Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research

Paper • 2402.00159 • Published Jan 31 • 59
The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only

Paper • 2306.01116 • Published Jun 1, 2023 • 31

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

Paper • 2307.13304 • Published Jul 25, 2023 • 2
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 • 17
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Paper • 2306.00978 • Published Jun 1, 2023 • 8

Papers: Quantization

FP8-LM: Training FP8 Large Language Models

Paper • 2310.18313 • Published Oct 27, 2023 • 31
LLM-FP4: 4-Bit Floating-Point Quantized Transformers

Paper • 2310.16836 • Published Oct 25, 2023 • 13
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

Neural Network Compression & Quantization

Tracks papers and links about neural network compression and quantization technics

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Paper • 2208.07339 • Published Aug 15, 2022 • 4
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Paper • 2210.17323 • Published Oct 31, 2022 • 7
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Paper • 2211.10438 • Published Nov 18, 2022 • 2
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Paper • 2306.00978 • Published Jun 1, 2023 • 8

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