The "LLM" toolkit provides a command-line utility and Python library for interacting with large language models. It enables users to run prompts from the terminal, store responses in SQLite databases, generate embeddings, and more. This comprehensive guide includes topics such as setup, usage, OpenAI models, other models, embeddings, plugins, model aliases, Python API, prompt templates, logging, related tools, CLI reference, contributing, and changelog.
llm-tool provides a command-line utility for running large language models locally. It includes scripts for pulling models from the internet, starting them, and managing them using various commands such as 'run', 'ps', 'kill', 'rm', and 'pull'. Additionally, it offers a Python script named 'querylocal.py' for querying these models. The repository also come
The article discusses the use of large language models (LLMs) as reasoning engines for powering agent workflows, focusing specifically on ReAct agents. It explains how these agents combine reasoning and action capabilities and provides examples of how they function. Challenges faced while implementing such agents are also mentioned, along with ways to overcome them. Additionally, the integration of open-source models within LangChain is highlighted.
This tutorial introduces promptrefiner, a tool created by Amirarsalan Rajabi that uses the GPT-4 model to create perfect system prompts for local LLMs.
Bookmarks on this page are managed by an admin user. 