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Collections including paper arxiv:2310.00035

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

Paper • 2310.08659 • Published Oct 12, 2023 • 25
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models

Paper • 2309.14717 • Published Sep 26, 2023 • 44
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers

Paper • 2309.16119 • Published Sep 28, 2023 • 1
LoRA ensembles for large language model fine-tuning

Paper • 2310.00035 • Published Sep 29, 2023 • 2

QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models

Paper • 2310.16795 • Published Oct 25, 2023 • 26
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference

Paper • 2308.12066 • Published Aug 23, 2023 • 4
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference

Paper • 2303.06182 • Published Mar 10, 2023 • 1
EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate

Paper • 2112.14397 • Published Dec 29, 2021 • 1

Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs

Paper • 2310.13961 • Published Oct 21, 2023 • 4
Diversity of Thought Improves Reasoning Abilities of Large Language Models

Paper • 2310.07088 • Published Oct 11, 2023 • 5
AutoMix: Automatically Mixing Language Models

Paper • 2310.12963 • Published Oct 19, 2023 • 14
SAI: Solving AI Tasks with Systematic Artificial Intelligence in Communication Network

Paper • 2310.09049 • Published Oct 13, 2023 • 1

MOE papers to read

Copied from MoE using https://huggingface.co/spaces/librarian-bots/collection_cloner.

QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models

Paper • 2310.16795 • Published Oct 25, 2023 • 26
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference

Paper • 2308.12066 • Published Aug 23, 2023 • 4
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference

Paper • 2303.06182 • Published Mar 10, 2023 • 1
EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate

Paper • 2112.14397 • Published Dec 29, 2021 • 1

Papers related to parameter efficient finetuning methods.

LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery

Paper • 2310.18356 • Published Oct 24, 2023 • 22
LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models

Paper • 2310.08659 • Published Oct 12, 2023 • 25
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers

Paper • 2309.16119 • Published Sep 28, 2023 • 1
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models

Paper • 2309.14717 • Published Sep 26, 2023 • 44

Papers: MoE/Ensemble

Papers related to Mixture of Experts topics.

QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models

Paper • 2310.16795 • Published Oct 25, 2023 • 26
Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs

Paper • 2310.13961 • Published Oct 21, 2023 • 4
The Consensus Game: Language Model Generation via Equilibrium Search

Paper • 2310.09139 • Published Oct 13, 2023 • 12
Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning

Paper • 2310.03094 • Published Oct 4, 2023 • 12

LoRA ensembles for large language model fine-tuning

Paper • 2310.00035 • Published Sep 29, 2023 • 2
LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models

Paper • 2310.08659 • Published Oct 12, 2023 • 25
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models

Paper • 2309.14717 • Published Sep 26, 2023 • 44
LoRA Learns Less and Forgets Less

Paper • 2405.09673 • Published May 15, 2024 • 88

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