By A Mystery Man Writer
Summary We created a guide for fine-tuning and evaluating LLMs using LangSmith for dataset management and evaluation. We did this both with an open source LLM on CoLab and HuggingFace for model training, as well as OpenAI's new finetuning service. As a test case, we fine-tuned LLaMA2-7b-chat and gpt-3.5-turbo for an extraction task (knowledge graph triple extraction) using training data exported from LangSmith and also evaluated the results using LangSmith. The CoLab guide is here. Context I
Applying OpenAI's RAG Strategies - nikkie-memos
Using LangSmith to Support Fine-tuning
Nicolas A. Duerr on LinkedIn: #business #strategy #partnerships
Nicolas A. Duerr on LinkedIn: #futurebrains #platform #marketplace #strategy #innovation
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LangChainのv0.0266からv0.0.276までの差分を整理(もくもく会向け)|mah_lab / 西見 公宏
LangChainのv0.0266からv0.0.276までの差分を整理(もくもく会向け)|mah_lab / 西見 公宏
Applying OpenAI's RAG Strategies 和訳|p
Thread by @LangChainAI on Thread Reader App – Thread Reader App
Using LangSmith to Support Fine-tuning
Using LangSmith to Support Fine-tuning
Multi-Vector Retriever for RAG on tables, text, and images 和訳|p