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
Nicolas A. Duerr on LinkedIn: #success #strategy #product #validation
Multi-Vector Retriever for RAG on tables, text, and images 和訳｜p
컴퓨터 vs 책: [B급 프로그래머] 8월 4주 소식(빅데이터/인공지능, 하드웨어, 읽을거리 부문)
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
컴퓨터 vs 책: 8월 2023
LangChain（0.0.340）官方文档十一：Agents之Agent Types_langchain agenttype-CSDN博客
LangSaaS - No Code LangChain SaaS - Product Information, Latest Updates, and Reviews 2024
Nicolas A. Duerr on LinkedIn: #success #strategy #product #validation
Nicolas A. Duerr on LinkedIn: #futurebrains #platform #marketplace #strategy #innovation
Using LangSmith to Support Fine-tuning
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
