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2025
【论文笔记】PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models
【论文笔记】PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models1
【论文笔记】Direct Preference Optimization: Your Language Model is Secretly a Reward Model
【论文笔记】Direct Preference Optimization: Your Language Model is Secretly a Reward Model2
【论文笔记】LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
【论文笔记】LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models3
【论文笔记】NEFTune: Noisy Embeddings Improve Instruction Finetuning
【论文笔记】NEFTune: Noisy Embeddings Improve Instruction Finetuning4
【论文笔记】QLoRA: Efficient Finetuning of Quantized LLMs
【论文笔记】QLoRA: Efficient Finetuning of Quantized LLMs5
2024
【论文笔记】Visual Prompt Tuning
【论文笔记】Visual Prompt Tuning6
【论文笔记】P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks
【论文笔记】P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks7
【论文笔记】LoRA: Low-Rank Adaptation of Large Language Models
【论文笔记】LoRA: Low-Rank Adaptation of Large Language Models8
【论文笔记】Prefix-Tuning: Optimizing Continuous Prompts for Generation
【论文笔记】Prefix-Tuning: Optimizing Continuous Prompts for Generation9
【论文笔记】The Power of Scale for Parameter-Efficient Prompt Tuning
【论文笔记】The Power of Scale for Parameter-Efficient Prompt Tuning10
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