This repository provides a detailed Jupyter Notebook demonstrating how to use and fine-tune large language models (LLMs) like Llama2 and Mistral7B via LangChain. The notebook is designed to guide users through the process of integrating these models with advanced LangChain features.
Learning Objectives By the end of this experiment, you will be able to:
Utilize open-source LLMs such as zephyr-7b-beta, Mistral-7B-Instruct-v0.2, and Llama2 through the Hugging Face Hub with LangChain. Understand and implement the concepts of Prompt Templates, Memory, and Output Parsers in LangChain.