QLoRA: Efficient fine-tuning strategies and practices for quantitative LLM - Code World

QLoRA: Efficient fine-tuning strategies and practices for quantitative LLM

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Origin blog.csdn.net/FrenzyTechAI/article/details/132686051

QLoRA: Efficient fine-tuning strategies and practices for quantitative LLM

[NLP] LLM efficient fine-tuning (PEFT)--LoRA

LoRA, AdaLoRA, QLoRA, a review of the principle of efficient fine-tuning technology for large model parameters

Lightweight fine-tuning Parameter-Efficient Fine-Tuning

LLMs: LLaMA Efficient Tuning (an efficient tool that can efficiently fine-tune [full parameters/LoRA/QLoRA] mainstream large models [ChatGLM2/LLaMA2/Baichuan, etc.] [pre-training + instruction supervision fine-tuning +

Detailed notes on fine-tuning Llama 2 using QLoRA

Efficient fine-tuning technology for large models

Fine-tuning LLM with a single GPU

Summary of LLM model fine-tuning methods

LLM - SFT workflow fine-tuning workflow

LLM FAQ (Fine-tuning section)

【LLM】Prompt tuning large model fine-tuning practice

Large language model fine-tuning and PEFT efficient fine-tuning

[Instruction fine-tuning of LLM series] Long story short, "Prompt" for instruction fine-tuning of large models

LaVIN—Efficient Instruction Fine-tuning for Multimodal Dialogue Models

Efficient fine-tuning practice of LLaMA, ChatGLM, BLOOM parameters

Efficient fine-tuning of large models - introduction to the PEFT framework

HuggingFace study notes--BitFit efficient fine-tuning

Summary of three fine-tuning techniques for pre-training large language models: fine-tuning, parameter-efficient fine-tuning and prompt-tuning

Overview of the principles of efficient fine-tuning technology for large model parameters (2) - BitFit, Prefix Tuning, Prompt Tuning

LLM: New paradigm of Prompt-Tuning/Instruction-tuning fine-tuning

Practice Sharing | Exploring InternLM2.5: Best Practices for PyTorch Model Fine-tuning

Popular understanding of major fine-tuning methods for large models: from Prefix Tuning, P-Tuning V1/V2 to LoRA, QLoRA

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

Under the overview of efficient fine-tuning of large models: DiffPruning, BitFit, LoRa, AdaLoRA, MAM Adapters, UniPELT

[Natural Language Processing] Efficient fine-tuning of large models: PEFT use cases

Simple understanding of LoRA (Low-Rank Adaptation) in efficient fine-tuning of large model parameters

Generative AI New World | Overview of the principles of efficient fine-tuning and quantification of large model parameters

LLMs Parameter efficient fine-tuning PEFT techniques 2: Soft prompts

[LLM] self-instruct construction instruction fine-tuning data set

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