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Collections including paper arxiv:2205.05638

Diversity of Thought Improves Reasoning Abilities of Large Language Models

Paper • 2310.07088 • Published Oct 11, 2023 • 5
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

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

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 • 9
Prefix-Tuning: Optimizing Continuous Prompts for Generation

Paper • 2101.00190 • Published Jan 1, 2021 • 6
GPT Understands, Too

Paper • 2103.10385 • Published Mar 18, 2021 • 8

bigcode/starcoder

Text Generation • Updated Oct 8 • 12.9k • • 2.81k
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Paper • 2205.05638 • Published May 11, 2022 • 3

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

Paper • 2403.03507 • Published Mar 6 • 182
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 • 9
In-Context Learning Demonstration Selection via Influence Analysis

Paper • 2402.11750 • Published Feb 19 • 2

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 • 2

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 • 22
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

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

Paper • 2310.08659 • Published Oct 12, 2023 • 22
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

Dissecting In-Context Learning of Translations in GPTs

Paper • 2310.15987 • Published Oct 24, 2023 • 5
In-Context Learning Creates Task Vectors

Paper • 2310.15916 • Published Oct 24, 2023 • 41
ZeroGen: Efficient Zero-shot Learning via Dataset Generation

Paper • 2202.07922 • Published Feb 16, 2022 • 1
Promptor: A Conversational and Autonomous Prompt Generation Agent for Intelligent Text Entry Techniques

Paper • 2310.08101 • Published Oct 12, 2023 • 1

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