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

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

정보 2023-06-12 11:06:05 독서 시간: null

NoSuchKey

추천

출처blog.csdn.net/qq_18555105/article/details/130224392

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

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

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

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

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

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

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

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

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

Practical Application of Large Models 16-A fine-tuning technique for large pre-trained models: Practical application of the Adapter-Tuning method, detailed introduction to the principle

LoRA fine-tuning stable diffusion models: principle and actual combat

Flow chart and pseudocode implementation of Prefix-tuning, Adapter, LLaMA-Adapter

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

Fine-tuning: In-depth analysis of the application of P-tuning v2 on large models

Efficient fine-tuning technology for large models

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

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

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

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

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

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

LaVIN—Efficient Instruction Fine-tuning for Multimodal Dialogue Models

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

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

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

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

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

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

Practical tips for fine-tuning large language models with LoRA

Distributed Inference and Fine-tuning of Large Language Models Over The Internet

추천

행

Jeecg 경험 기사 -이 세상은 프로그래머의 부족,하지만 건축업자와 건축가의 부족은 없다

중소기업에 공식 웹사이트와 도움말 센터가 필요합니까? 왜 그런 말을 해?

타이프 라이터 환경 설치 및 아이디어 개발 환경의 구성

MySQL-- 건축 기사를 얻기

기업 -Shell 케이스 (12) - MySQL의 데이터베이스 백업 스크립트

세계 - 간단한 - 압축 풀기 - UPX 포격에 공격과 방어

(SpringBoot) Chapter 5: SpringBoot creation and use

IDEA는 개발 효율성을 높이기 위해 일반적으로 사용되는 플러그인

의 "+"의에 문자열 과부하

JS 마이크로 채널 구성 인터페이스는 보안 도메인 이름을 구성하는 -Nginx

아카이브

기타

2020-04-08(1460)

2020-04-07(1517)

2020-04-06(1499)

2020-04-05(1440)

2020-04-04(1629)

2020-04-03(1644)

2020-04-02(1572)

2020-04-01(1665)

2020-03-31(1639)

2020-03-30(1334)