LangChain and LangSmith: A dual strategy for building and fine-tuning smart applications that support LLM - Code World

LangChain and LangSmith: A dual strategy for building and fine-tuning smart applications that support LLM

News 2023-09-12 03:37:24 views: null

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Origin blog.csdn.net/FrenzyTechAI/article/details/132695264

LangChain and LangSmith: A dual strategy for building and fine-tuning smart applications that support LLM

[LLM] Basic use of LangChain (building LLM applications)

ChatGPT empowers low-code development: dual engines for building smart applications

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

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

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

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

LangChain и LangSmith: двойная стратегия создания и тонкой настройки интеллектуальных приложений, поддерживающих LLM

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

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

【LLM】Financial large model scene and large model Lora fine-tuning practice

【CS324】LLM (large model capabilities, data, architecture, distributed training, fine-tuning, etc.)

Common techniques in LLM large language model training: fine-tuning and embedding

LLM fine-tuning (3) | Analysis of RLHF + Reward Model + PPO technology in large models

[Architecture Practice] How to integrate LLM large model capabilities into enterprise applications through langchain?

LLM FAQ (LangChain section)

LangChain develops a framework for LLM

LangChain y LangSmith: una estrategia dual para crear y ajustar aplicaciones inteligentes que admitan LLM

Dual power strategy

LLM-Large Model Training-Step (3): Instruction fine-tuning [Superviser Fine-Tuning] [Chinese instruction corpus] [Training method is the same as unsupervised learning] [Instruction corpus style: instruction+input+output]

Introduction to Apache Mahout: Building smart applications through scalable, business-friendly machine learning

LoRA fine-tuning

PEFT fine-tuning

javascript strategy mode applications!

Building Production-Ready LLM Applications with LlamaIndex: Document Metadata for Higher Accuracy Retrieval The Importance of Document Metadata in Quality Retrieval (Tutorial with Source Code)

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

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