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Collections including paper arxiv:2103.10385

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

Paper • 2310.08659 • Published Oct 12, 2023 • 21
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models

Paper • 2309.14717 • Published Sep 26, 2023 • 43
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

Combining Modular Skills in Multitask Learning

Paper • 2202.13914 • Published Feb 28, 2022 • 4
The Power of Scale for Parameter-Efficient Prompt Tuning

Paper • 2104.08691 • Published Apr 18, 2021 • 8
Prefix-Tuning: Optimizing Continuous Prompts for Generation

Paper • 2101.00190 • Published Jan 1, 2021 • 5
GPT Understands, Too

Paper • 2103.10385 • Published Mar 18, 2021 • 6

Ordered List of Resources to understand soft prompting while covering the basics of discrete prompting as well.

It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners

Paper • 2009.07118 • Published Sep 15, 2020 • 1
Prefix-Tuning: Optimizing Continuous Prompts for Generation

Paper • 2101.00190 • Published Jan 1, 2021 • 5
The Power of Scale for Parameter-Efficient Prompt Tuning

Paper • 2104.08691 • Published Apr 18, 2021 • 8
GPT Understands, Too

Paper • 2103.10385 • Published Mar 18, 2021 • 6

NLP Parameter Efficient Finetuning

Explore research optimizing NLP models for maximum performance with minimal computational resources.

Efficient Few-Shot Learning Without Prompts

Paper • 2209.11055 • Published Sep 22, 2022 • 2
Parameter-Efficient Transfer Learning for NLP

Paper • 1902.00751 • Published Feb 2, 2019 • 1
GPT Understands, Too

Paper • 2103.10385 • Published Mar 18, 2021 • 6
The Power of Scale for Parameter-Efficient Prompt Tuning

Paper • 2104.08691 • Published Apr 18, 2021 • 8

A collection of methods that have been implemented in the 🤗 PEFT library

Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Paper • 2303.10512 • Published Mar 18, 2023
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Paper • 2205.05638 • Published May 11, 2022 • 3
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

Paper • 2303.16199 • Published Mar 28, 2023 • 4
FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning

Paper • 2108.06098 • Published Aug 13, 2021 • 1

Diversity of Thought Improves Reasoning Abilities of Large Language Models

Paper • 2310.07088 • Published Oct 11, 2023 • 4
Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning

Paper • 2310.04474 • Published Oct 6, 2023 • 2
Promptor: A Conversational and Autonomous Prompt Generation Agent for Intelligent Text Entry Techniques

Paper • 2310.08101 • Published Oct 12, 2023 • 1
Instance Needs More Care: Rewriting Prompts for Instances Yields Better Zero-Shot Performance

Paper • 2310.02107 • Published Oct 3, 2023 • 3

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