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

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

企业开发 2023-06-21 22:48:04 阅读次数: 0

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转载自blog.csdn.net/kebijuelun/article/details/129862556

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

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

【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

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

轻量化微调 Parameter-Efficient Fine-Tuning

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

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

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

Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

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

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

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

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

LLMs参数高效微调（PEFT） Parameter efficient fine-tuning (PEFT)

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

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

【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

LLaMA-Adapter

深度学习论文: Rethinking Mobile Block for Efficient Attention-based Models及其PyTorch实现

LLMs PEFT技术1：LoRA Parameter efficient fine-tuning PEFT techniques 1: LoRA Low rank Adaptation

LLMs 参数高效微调技术2：软提示 Parameter efficient fine-tuning PEFT techniques 2: Soft prompts

微调大模型（Finetuning Large Language Models）—Training tuning（五）

Fine-tuning

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