LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-into Attention论文解读 - 代码天地

LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-into Attention论文解读

业界资讯 2023-06-12 06:36:26 阅读次数: 0

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转载自blog.csdn.net/qq_18555105/article/details/130224392

LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-into Attention论文解读

LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

【论文笔记】BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

Fine-Tuning Language Models from Human Preferences

Distributed Inference and Fine-tuning of Large Language Models Over The Internet

LLaMA Open and Efficient Foundation Language Models

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论文分享|《Universal Language Model Fine-tuning for Text Classificatio》

一个开源的大型语言模型LLaMA论文简单解读，LLaMA: Open and Efficient Foundation Language Models

Arixv 2403 | Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

【LLM系列之LLaMA】LLaMA: Open and Efficient Foundation Language Models

AMiner推荐论文：Hierarchical Transformers Are More Efficient Language Models

论文略读：MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning

【论文笔记】ULMFiT——Universal Language Model Fine-tuning for Text Classification

Re73 读论文：ULMFiT Universal Language Model Fine-tuning for Text Classification

轻量化微调 Parameter-Efficient Fine-Tuning

《Universal Language Model Fine-tuning for Text Classification》翻译

Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

论文解读：Large Language Models as Analogical Reasoners

A Survey on Multimodal Large Language Models论文解读

SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

LONGQLORA: EFFICIENT AND EFFECTIVE METHOD TO EXTEND CONTEXT LENGTH OF LARGE LANGUAGE MODELS

论文阅读9-Fine-tuning Pre-Trained Transformer Language Models to(远程监督关系抽取,ACL2019,GPT,长尾关系,DISTRE）

预训练大语言模型的三种微调技术总结：fine-tuning、parameter-efficient fine-tuning和prompt-tuning

Prefix-tuning、Adapter、LLaMA-Adapter的流程图与伪代码实现

论文Bridging Vision and Language Encoders: Parameter-Efficient Tuning for Referring Image Segmentation

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将迁移学习用于文本分类《 Universal Language Model Fine-tuning for Text Classification》

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