Using LangSmith to Support Fine-tuning
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
LangChain on X: OpenAI just made finetuning as easy an API call But there's still plenty of hard parts - top of mind are *dataset curation* and *evaluation* We shipped an end-to-end
Multi-Vector Retriever for RAG on tables, text, and images 和訳｜p
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Nicolas A. Duerr on LinkedIn: #business #strategy #partnerships
Nicolas A. Duerr on LinkedIn: #success #strategy #product #validation
Nicolas A. Duerr on LinkedIn: #karlsruhe #networking #learning #business
컴퓨터 vs 책: 8월 2023
Nicolas A. Duerr on LinkedIn: #business #strategy #partnerships
Week of 8/21] LangChain Release Notes
🧩DemoGPT (@demo_gpt) / X
🧩DemoGPT (@demo_gpt) / X
Multi-Vector Retriever for RAG on tables, text, and images 和訳｜p
LangSaaS - No Code LangChain SaaS - Product Information, Latest Updates, and Reviews 2024
LangChain（0.0.340）官方文档十一：Agents之Agent Types_langchain agenttype-CSDN博客
