Introduction to the JARVIS Project
The JARVIS project is a forward-thinking initiative focused on the exploration of artificial general intelligence (AGI). It aims to push the boundaries of what's possible with AI and make its findings accessible to the broader research community. Here's a detailed look at the JARVIS project, its recent developments, and its operational setup.
Recent Developments
JARVIS continually evolves with new tools and enhancements. Here are some of the latest updates:
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EasyTool (Released 2024.01.15): Made to streamline the use of tools in large language models (LLMs), EasyTool offers code and datasets designed for this purpose. More information can be found in the paper, "EasyTool: Enhancing LLM-based Agents with Concise Tool Instruction."
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TaskBench (Released 2023.11.30): This tool evaluates the task automation capabilities of LLMs. It comes with accompanying code and datasets, detailed in the paper, "TaskBench: Benchmarking Large Language Models for Task Automation."
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Upcoming Jarvis Version (Planned as of 2023.07.28): A new version of Jarvis is on its way, focusing on evaluation and project rebuilding.
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Light Langchain Version (Released 2023.07.24): A simplified version of Jarvis that's part of the Langchain ecosystem is now available.
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Integration with Azure and GPT-4 (Released 2023.04.16): Jarvis now supports OpenAI's services on the Azure platform, including the powerful GPT-4 model.
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Gradio Demo and Web API (Released 2023.04.06): A demonstration using Gradio is hosted on Hugging Face, along with a web API for handling various stages of task and result processing.
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CLI Mode (Released 2023.04.03): A command-line interface version is available to allow users to interact with Jarvis without needing to deploy models locally.
System Overview
JARVIS uses language as a bridge to connect various AI models within the LLM framework, simplifying the process of tackling complex AI tasks. The project is centered around a collaborative system that includes a language model as the controller and many expert models acting as collaborators. The workflow of this system is structured into four key stages:
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Task Planning: ChatGPT is used to analyze user requests, breaking them down into manageable tasks based on user intent.
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Model Selection: Expert models from Hugging Face are selected by ChatGPT for solving the planned tasks.
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Task Execution: Each chosen model is executed, and the results are returned to ChatGPT.
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Response Generation: ChatGPT synthesizes all model predictions to generate comprehensive responses for users.
System Requirements
For effective operation, JARVIS has certain system requirements depending on the deployment configuration:
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Default Configuration: Requires Ubuntu 16.04 or higher, with high RAM and VRAM specifications.
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Lite Configuration: More accessible, requiring only Ubuntu 16.04, and doesn’t necessitate local model deployment.
Quick Start Guide
To get started with JARVIS, you need to have an OpenAI Key and a Hugging Face Token. Once they are set, the system can be run in various environments such as server, web, or CLI modes. Detailed steps are available for each method, ensuring flexibility in how users can deploy and interact with JARVIS.
Use Cases
JARVIS can be employed in different scenarios, whether it's providing development environments for embedding device support like NVIDIA Jetson, exploring AI capabilities in a research setting, or interacting through intuitive web interfaces.
Conclusion
JARVIS stands as a beacon of innovation in the landscape of artificial general intelligence research. By fostering a collaborative AI ecosystem, it not only advances research but also democratizes access to AI solutions, paving the way for more intuitive and effective human-machine interactions.