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

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

Internet 2023-09-30 18:48:36 views: null

NoSuchKey

Guess you like

Origin blog.csdn.net/zgpeace/article/details/133003844

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

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

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

Lightweight fine-tuning Parameter-Efficient Fine-Tuning

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

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

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

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

PEFT fine-tuning

Tips for using the Peft library (2): Delete and merge fine-tuning parameters [remove the base model parameters (freeze) from the model parameters after full-parameter fine-tuning, and then publish this part of the parameter module trained by yourself]

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 +

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

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

LLMs instruction fine-tuning Instruction fine-tuning

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

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

Meta proposes a new parameter efficient fine-tuning scheme, only one RNN is needed, and the GPU usage of the Transformer model is reduced by 84%!

Summary of fine-tuning techniques for large models

ChatGLM Efficient Tuning efficiency debugging PEFT

Efficient fine-tuning technology for large models

LLMs multi-task instruction fine-tuning Multi-task instruction fine-tuning

Play Llama2 fast! Alibaba Cloud Machine Learning PAI launched the best practice (2) - full parameter fine-tuning training

ChatGLM server deployment fine-tuning (2)

LaVIN—Efficient Instruction Fine-tuning for Multimodal Dialogue Models

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

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

HuggingFace study notes--BitFit efficient fine-tuning

[Technical Talk] What is full parameter fine-tuning?

ChatGLM2 of LLMs: Introduction and usage of ChatGLM-Finetuning (based on DeepSpeed) (four fine-tuning methods (Freeze method/Lora method/P-Tuning method/full parameters) + single-card/multi-card training

Chinese-LLaMA-Alpaca-2 of LLMs: source code interpretation (run_clm_pt_with_peft.py file) - model training pre-work (parameter analysis + configuration log) → model initialization (detecting whether there is a trained chec

Recommended

Ranking

Serialization TLV of Communication Protocol

sklearn stepping on confusion_matrix and KFold

ES6 中的 Iterator 迭代器

win10离线安装.NET Framework 3.5教程

Media Promotion Traps Revealed! MediaEasy helps you successfully avoid pitfalls!

2021年9月国产数据库大事记-墨天轮

2. Introduction to Java [IDEA]

HJ17 coordinate movement

Has something replaced BeanItemContainer in Vaadin 8?

leetcode-205- string isomorphic

Daily

More

2025-03-19(0)

2025-03-18(0)

2025-03-17(0)

2025-03-16(0)

2025-03-15(0)

2025-03-14(0)

2025-03-13(0)

2025-03-12(0)

2025-03-11(0)

2025-03-10(0)