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
컴퓨터 vs 책: [B급 프로그래머] 8월 4주 소식(빅데이터/인공지능, 하드웨어, 읽을거리 부문)
🧩DemoGPT (@demo_gpt) / X
Nicolas A. Duerr on LinkedIn: #success #strategy #product #validation
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
LangSaaS - No Code LangChain SaaS - Product Information, Latest Updates, and Reviews 2024
컴퓨터 vs 책: [B급 프로그래머] 8월 4주 소식(빅데이터/인공지능, 하드웨어, 읽을거리 부문)
Multi-Vector Retriever for RAG on tables, text, and images 和訳｜p
Thread by @LangChainAI on Thread Reader App – Thread Reader App
Week of 8/21] LangChain Release Notes
Nicolas A. Duerr on LinkedIn: #business #strategy #partnerships
Thread by @LangChainAI on Thread Reader App – Thread Reader App
Nicolas A. Duerr on LinkedIn: #business #strategy #partnerships