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Collections including paper arxiv:2104.08691

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

GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Paper • 2403.03507 • Published Mar 6 • 172
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Paper • 2205.05638 • Published May 11, 2022 • 3
The Power of Scale for Parameter-Efficient Prompt Tuning

Paper • 2104.08691 • Published Apr 18, 2021 • 6
In-Context Learning Demonstration Selection via Influence Analysis

Paper • 2402.11750 • Published Feb 19 • 2

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 • 3
The Power of Scale for Parameter-Efficient Prompt Tuning

Paper • 2104.08691 • Published Apr 18, 2021 • 6
GPT Understands, Too

Paper • 2103.10385 • Published Mar 18, 2021 • 3

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 • 3
The Power of Scale for Parameter-Efficient Prompt Tuning

Paper • 2104.08691 • Published Apr 18, 2021 • 6

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 566
When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method

Paper • 2402.17193 • Published Feb 27 • 23
Training-Free Long-Context Scaling of Large Language Models

Paper • 2402.17463 • Published Feb 27 • 17
The Power of Scale for Parameter-Efficient Prompt Tuning

Paper • 2104.08691 • Published Apr 18, 2021 • 6

The Power of Scale for Parameter-Efficient Prompt Tuning

Paper • 2104.08691 • Published Apr 18, 2021 • 6
QLoRA: Efficient Finetuning of Quantized LLMs

Paper • 2305.14314 • Published May 23, 2023 • 41
A Closer Look at the Limitations of Instruction Tuning

Paper • 2402.05119 • Published Feb 3 • 5

Interesting AI papers

Attention Is All You Need

Paper • 1706.03762 • Published Jun 12, 2017 • 34
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Paper • 1810.04805 • Published Oct 11, 2018 • 11
Universal Language Model Fine-tuning for Text Classification

Paper • 1801.06146 • Published Jan 18, 2018 • 6
Language Models are Few-Shot Learners

Paper • 2005.14165 • Published May 28, 2020 • 9

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