A Survey of Fine-tuning Methods for Large Models - Code World

A Survey of Fine-tuning Methods for Large Models

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Origin blog.csdn.net/lovely_yoshino/article/details/130778577

A Survey of Fine-tuning Methods for Large Models

Summary of fine-tuning methods for large models: LoRA, Adapter, Prefix-tuning, P-tuning, Prompt-tuning

Summary of fine-tuning techniques for large models

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Efficient fine-tuning technology for large models

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

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

LLaVA: Bringing Visual Fine-tuning to Large Models

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

Practical tips for fine-tuning large language models with LoRA

Paper: Translation and Interpretation of "Instruction Tuning for Large Language Models: A Survey—A Survey of Instruction Tuning for Large Language Models"

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

Artificial intelligence large language model fine-tuning technology: SFT, LoRA, Freeze supervised fine-tuning methods

In the era of AIGC, how can fine-tuning of large models play the most important role?

LoRA: A Low-Rank Adaptive Fine-tuning Model for Large Models

Full-parameter fine-tuning of large language models with limited resources

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

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The secret weapon for efficient development of large models: MindSpore PET, a low-parameter fine-tuning kit for large models

On the overview of efficient fine-tuning of large models: Adapter Tuning, AdaMix, PET, Prefix-Tuning, Prompt Tuning, P-tuning, P-tuning v2

[Natural Language Processing] [Large Model] LoRA and BLOOM-LORA implementation codes for fine-tuning large model methods with very low resources

Practical application of large models 10 - Detailed explanation of large model domain knowledge and parameter efficient fine-tuning (PEFT) technology, and use PEFT to train your own large models

Interpretation of Lawyer LLaMA, fine-tuning of large models in Yanshen's professional field: data set construction, model training

Paper Reading A Survey of Large Language Models 1

Paper Reading A Survey of Large Language Models 2

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