Models
Datasets
Spaces
Buckets new
Docs
Enterprise
Pricing
Log In
Sign Up

Collections

Discover the best community collections!

Collections including paper arxiv:2307.13269

Experts Weights Averaging: A New General Training Scheme for Vision Transformers

Paper • 2308.06093 • Published Aug 11, 2023 • 2
Platypus: Quick, Cheap, and Powerful Refinement of LLMs

Paper • 2308.07317 • Published Aug 14, 2023 • 25
Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision Transformers

Paper • 2211.11315 • Published Nov 21, 2022 • 1
LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition

Paper • 2307.13269 • Published Jul 25, 2023 • 34

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

Paper • 2310.08659 • Published Oct 12, 2023 • 29
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models

Paper • 2309.14717 • Published Sep 26, 2023 • 46
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

From Sparse to Soft Mixtures of Experts

Paper • 2308.00951 • Published Aug 2, 2023 • 22
LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition

Paper • 2307.13269 • Published Jul 25, 2023 • 34
S-LoRA: Serving Thousands of Concurrent LoRA Adapters

Paper • 2311.03285 • Published Nov 6, 2023 • 30

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

Paper • 2310.16795 • Published Oct 25, 2023 • 27
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

Model Merging is a very popular technique nowadays in LLM. Here is a chronological list of papers on the space that will help you get started with it!

Qualitatively characterizing neural network optimization problems

Paper • 1412.6544 • Published Dec 19, 2014 • 4
Convergent Learning: Do different neural networks learn the same representations?

Paper • 1511.07543 • Published Nov 24, 2015 • 2
Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models

Paper • 1909.11299 • Published Sep 25, 2019 • 2
Model Fusion via Optimal Transport

Paper • 1910.05653 • Published Oct 12, 2019 • 1

Experts Weights Averaging: A New General Training Scheme for Vision Transformers

Paper • 2308.06093 • Published Aug 11, 2023 • 2
Platypus: Quick, Cheap, and Powerful Refinement of LLMs

Paper • 2308.07317 • Published Aug 14, 2023 • 25
Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision Transformers

Paper • 2211.11315 • Published Nov 21, 2022 • 1
LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition

Paper • 2307.13269 • Published Jul 25, 2023 • 34

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

Paper • 2310.16795 • Published Oct 25, 2023 • 27
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

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

Paper • 2310.08659 • Published Oct 12, 2023 • 29
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models

Paper • 2309.14717 • Published Sep 26, 2023 • 46
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

Model Merging is a very popular technique nowadays in LLM. Here is a chronological list of papers on the space that will help you get started with it!

Qualitatively characterizing neural network optimization problems

Paper • 1412.6544 • Published Dec 19, 2014 • 4
Convergent Learning: Do different neural networks learn the same representations?

Paper • 1511.07543 • Published Nov 24, 2015 • 2
Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models

Paper • 1909.11299 • Published Sep 25, 2019 • 2
Model Fusion via Optimal Transport

Paper • 1910.05653 • Published Oct 12, 2019 • 1

From Sparse to Soft Mixtures of Experts

Paper • 2308.00951 • Published Aug 2, 2023 • 22
LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition

Paper • 2307.13269 • Published Jul 25, 2023 • 34
S-LoRA: Serving Thousands of Concurrent LoRA Adapters

Paper • 2311.03285 • Published Nov 6, 2023 • 30

Company

TOS Privacy About Careers

Website

Models Datasets Spaces Pricing Docs