Summary

JARVIS is an open-source AI platform developed by Microsoft that fosters collaboration between Language Model Managers (LLMs) and the machine learning community. It empowers LLMs to share their models, receive valuable feedback, and access a rich ecosystem of existing language models. By facilitating communication and knowledge exchange, JARVIS aims to accelerate the development and adoption of powerful language models for real-world applications.

Key Features

• Model Publication and Feedback: LLMs can easily publish their models on JARVIS, allowing them to gain insights from the broader ML community through feedback and suggestions.
• Model Discovery and Exploration: Users can search for existing language models, understand their functionalities, and explore how they are being used in diverse applications.
• Open-Source Collaboration: JARVIS encourages community involvement through its open-source nature, allowing developers and researchers to contribute to its development and expansion.

Use Cases

• LLMs: Share their models, receive feedback on their performance and potential improvements, and gain insights into how their models are being used in various applications.
• ML Researchers: Explore the latest advancements in language modeling, discover potential use cases for their research, and contribute to the development of more effective language models.
• Developers: Integrate existing language models into their applications, explore new model functionalities, and contribute to the growth of the language model ecosystem.

Target User

JARVIS is primarily aimed at individuals and teams working with language models, including:

• Language Model Managers (LLMs): Responsible for creating and deploying language models for various applications.
• Machine Learning Researchers: Investigating and developing novel language modeling techniques.
• Software Developers: Utilizing language models to enhance their applications and build innovative solutions.

Overall, JARVIS serves as a critical bridge between language model creators and the broader machine learning community, promoting knowledge sharing, collaborative innovation, and the development of more powerful and impactful language models.

The technical architecture and evaluation of JARVIS are detailed in a paper published on arXiv. The tool is hosted on GitHub, where developers and researchers can access and contribute to its further development.