How to reduce model cost? Platypus: Fast, cheap and powerful LLM that beats the competition with only one GPU and 5 hours of LLaMA2 fine-tuning - 코드 세계

How to reduce model cost? Platypus: Fast, cheap and powerful LLM that beats the competition with only one GPU and 5 hours of LLaMA2 fine-tuning

발 2023-09-05 17:59:44 독서 시간: null

NoSuchKey

추천

출처blog.csdn.net/iCloudEnd/article/details/132663428

How to reduce model cost? Platypus: Fast, cheap and powerful LLM that beats the competition with only one GPU and 5 hours of LLaMA2 fine-tuning

How to reduce model cost? Platypus: Fast, cheap and powerful LLM that beats the competition with only one GPU and 5 hours of LLaMA2 fine-tuning

How to reduce model cost? Platypus: Fast, cheap and powerful LLM that beats the competition with only one GPU and 5 hours of LLaMA2 fine-tuning

How to reduce model cost? Platypus: Fast, cheap and powerful LLM that beats the competition with only one GPU and 5 hours of LLaMA2 fine-tuning

How to reduce model cost? Platypus: Fast, cheap and powerful LLM that beats the competition with only one GPU and 5 hours of LLaMA2 fine-tuning

How to reduce model cost? Platypus: Fast, cheap and powerful LLM that beats the competition with only one GPU and 5 hours of LLaMA2 fine-tuning

How to reduce model cost? Platypus: Fast, cheap and powerful LLM that beats the competition with only one GPU and 5 hours of LLaMA2 fine-tuning

How to reduce model cost? Platypus: Fast, cheap and powerful LLM that beats the competition with only one GPU and 5 hours of LLaMA2 fine-tuning

How to fine-tune the large medical model llm: llama2 study notes

Mistral 7B Large Language Model — Small but powerful (better than LLAMA2 13B!) Explore the unique architecture of Mistral 7B LLM and the unparalleled performance of GGLU (CPU) and GPU versions

(2) ChatGLM-6B model deployment and ptuning fine-tuning detailed tutorial

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

Databricks open source LLM, training only takes three hours and $30

Hong Kong Chinese & Soochow University released GrammarGPT, a large-scale Chinese grammar error correction model | It can achieve SOTA performance with only 1K data for instruction fine-tuning!

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

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

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

[Large model] Will chatGPT eventually become the only one company?

[NLP]LLM---FineTune의 자체 Llama2 모델

Platypus: fast, cheap and powerful large models

How to enhance the learning ability of large language model LLM?

How much do you know about large language model LLM?

LLM: LLaMA Efficient Tuning([전체 매개변수/LoRA/QLoRA] 주류 대형 모델[ChatGLM2/LLaMA2/Baichuan 등)을 효율적으로 미세 조정할 수 있는 효율적인 도구 [사전 훈련 + 지시 감독 미세 조정 +

Fast as Lightning ⚡: Yolov5-TensorRT-Ubuntu-Clion in one go

Tutorial on deploying Llama2 (MetaAI) large model under Linux system

How to pass only one person Name and pass the person Id in Vue

Organize 2 hours to tell you how to add text to the video tutorial

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%!

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

llama.cpp LLM 모델 Windows cpu 설치 및 배포, LLaMA2 모델 테스트 실행

추천

행

Mongo Save和Insert的区别

OpenCV的cornerSubPix函数

최소 부분 배열 순서의 길이를 필요

Hikvision 카메라 모델 선택 가이드

노트의 ngnix

비상시 선반에서 꺼내고, AI는 가짜 별과 함께 노래하고, 하버드 로스쿨 전문가들은 AI와 저작권법에 대해 이야기합니다.

메시지 대기열을 밀거나 당기십시오. RocketMQ와 Kafka는 어떻게합니까?

부트 스트랩 선택 2 모달 상자에서 문제를 해결할 수있는 검색 상자를 입력

단일 정밀도 부동 소수점 형 플로트 이유는 정확히 실수를 나타내지

음악은 자신이 데이터베이스에 연결 모듈을 썼다 호출 MusicRj

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)