LLM experiments with LangChain
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Updated
May 27, 2024 - Jupyter Notebook
LLM experiments with LangChain
The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). Allowing users to chat with LLM models, execute structured function calls and get structured output. Works also with models not fine-tuned to JSON output and function calls.
Multi-node production AI stack. Run the best of open source AI easily on your own servers. Create your own AI by fine-tuning open source models. Integrate LLMs with APIs. Run gptscript securely on the server
Start building LLM-empowered multi-agent applications in an easier way.
A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
Integrating AI into every workflow with our open-source, no-code platform, powered by the actor model for dynamic, graph-based solutions.
[CVPR2024 Highlight] Editable Scene Simulation for Autonomous Driving via LLM-Agent Collaboration
A curated list of Generative AI tools, works, models, and references
An intuitive approach to building with LLMs
Various OpenAI projects and experiments implemented in Python. Utilizes all OpenAI APIs.
Harness LLMs with Multi-Agent Programming
总结Prompt&LLM论文,开源数据&模型,AIGC应用
Sotopia: an Open-ended Social Learning Environment (ICLR 2024 spotlight)
Backend server for AGI OS - Awesome Gamer Insight Orchestrating System
langchain 工具,流程设计组件,服务,代理以及相关学习文档的合集(agent,service,tutorials,flow-design)
Design, conduct and analyze results of AI-powered surveys and experiments. Simulate social science and market research with large numbers of AI agents and LLMs.
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