Project Falcon OS: An Open Source LLM Operating System

This is the first blog post in a series of posts that will document my efforts in the Falcon OS project.
Created on December 4|Last edited on March 27
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When the Technology Innovation Institute based in the UAE announced the release of their Falcon 40B model in May 2023 it had an outsized impact on my professional life. As a Solutions Architect for Generative AI (GenAI) at AWS I worked with customers to build solutions with Large Language Models (LLMs), and many organizations were interested in hosting open-source models in their private (cloud) environment rather than sending requests to a public-facing API over the internet.
The problem at that time for me was that open-source LLMs were just not good enough for many common customer use cases. The Falcon model changed this: It was the first open-source LLM with a commercial license that was robust enough for customer demos.
Launch Announcement
Introducing Falcon 40BFalcon-40B is a 40 billion parameter causal decoder-only model that is available under the Apache 2.0 license, which permits commercial use without royalties or restrictions. At its release it stood out as the leading open-source model available, surpassing others like LLaMA, StableLM, RedPajama, and MPT.
Falcon is multilingual, supporting English, German, Spanish, French (and limited capabilities in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish). 
One of the core differences in the development of Falcon was the quality of the training data. The size of the pre-training data collected for Falcon 40B was nearly one trillion tokens gathered from public web crawls (~80%), research papers, legal text, news, literature, and social media conversations. Since LLMs are particularly sensitive to the data they are trained on, the team built a custom data pipeline to extract high-quality pre-training data using extensive filtering and deduplication, implemented both at the sample and string levels. The Falcon team just recently released their comprehensive research paper that details the data curation and training process:
Falcon Research Paper
Falcon's capabilities were a game changer for me - finally, I was able to build customer demos for popular use cases such as a private document chatbot:
Document Chatbot with Falcon-40B
Short demo: https://www.linkedin.com/posts/mtmancuso_aws-genai-awsswitzerland-activity-7079911261156585472-Wl17/﻿
GitHub repo: https://github.com/marshmellow77/falcon-document-chatbot﻿
The Falcon 40B Call For ProposalsUpon realizing how popular the model was, in June 2023, the Falcon team launched a Call for Proposals, inviting scientists, researchers, and innovators to submit ideas for impactful use cases and applications. The most exceptional proposals would receive an investment of training compute power to work on shaping transformative solutions with the powerful model.
Falcon 40B Call for Proposals
My Proposal: Falcon OSMy proposal was to develop an “operating system” with Falcon 48B at its core processing unit: "Falcon OS". This is similar to concepts Andrej Karpathy has outlined for LLMs acting as the orchestrating brain of an AI system. Given the open-source nature of Falcon, I wanted to explore using it in this capacity rather than a closed, proprietary model like GPT-4.
LLM OS from Andrej's video
Falcon OS could serve as an interface between the user and the computer, enabling the user to perform various tasks through natural language processing. This could include:
Text Processing: It can read and generate text, handling complex language tasks far beyond simple command-line instructions.
Knowledge Base: It will have extensive knowledge across various subjects, more than any single human, by accessing vast databases like an organization's customer orders, document stores like internal wikis, and the internet.
Software Interaction: The LLM OS will interact with existing software infrastructure, like calculators, Python interpreters, terminals, etc., leveraging these tools to execute tasks.
Multimedia Capabilities: It can process and generate multimedia content such as images, video, and music.
Communication: The LLM OS will be able to communicate with other instances of LLMs, possibly for distributed computing or enhanced functionality.
Learning and Adaptation: It will have the ability to learn from interactions and improve over time, possibly using reinforcement learning or other machine learning techniques.
Customization: Users can fine-tune it for specific tasks, and it might be available in various versions tailored to different applications or user preferences.
Overall, Falcon OS has the potential to lead to a future where the Falcon model is not just a tool for specific tasks but is woven into the fabric of how computers are operated and interacted with, making it more intuitive and powerful for a wide range of applications.
TII Announces The Falcon 40B FinalistsA few weeks ago, the Technology Innovation Institute (TII) announced the finalists, and I am thrilled and honoured that my proposal was shortlisted into the top 5:
Honoured to be a finalist
As a finalist, I receive training compute credits, which I can use to pursue my idea of Falcon OS 🤗
Potential ImpactI believe that the potential impact of Falcon OS on organizations could be substantial. By integrating an open-source language model at its core, Falcon OS presents an opportunity for businesses and institutions to develop advanced AI-driven applications without the constraints of proprietary systems. This freedom could lead to a surge in innovation, as developers and companies can customize and fine-tune the OS to their specific needs and their custom data, creating an interface powered by an LLM. Potential use cases include:
Automated Customer Support: Offering sophisticated customer service through natural language understanding, handling inquiries, troubleshooting issues, and providing solutions with fewer human interventions.
Software Development: Assisting programmers by interpreting natural language requests into code, debugging, code reviews, and providing recommendations for optimizations.
Personalized Education: Tailoring educational content to individual learning styles and paces, interpreting questions, and providing explanations, tutorials, or resources in response.
Research and Data Analysis: Automating literature reviews, data collection, analysis, and summarising findings across various disciplines.
Healthcare Management: Assisting with diagnosis, treatment options, patient monitoring, and managing administrative tasks by interpreting medical data and literature.
Creative Arts: Generating music, artwork, and literature or assisting creatives by providing insights or enhancing their work with AI-generated content.
Accessibility: Assisting individuals with disabilities by transcribing speech, describing visual content, or enabling control of devices through natural language.
Language Translation: Providing real-time, context-aware translation services for global communication without the need for human translators.
Enterprise Resource Planning: Integrating with business systems to manage operations, supply chains, and customer relationships through conversational interfaces.
Smart Home and IoT Integration: Managing smart home devices through voice commands, scheduling, and automation by understanding and predicting user preferences and needs.
Security: Monitoring and analyzing network traffic for potential threats using natural language commands and receiving intuitive explanations of complex security issues.
Financial Analysis: Interpreting market data, providing investment insights, and personal finance advice by understanding complex economic conditions and individual financial goals.
Content Creation: Assisting writers, journalists, and content creators by generating drafts, suggesting edits, and researching topics.
The inclusive nature of Falcon, supported by its permissive licensing, paves the way for a more democratized AI landscape. This accessibility means that even smaller organizations with limited resources can experiment with and benefit from this technology and this project. Consequently, Falcon OS has the potential to be a game-changer, leveling the playing field in the AI domain and fostering a community-driven ecosystem of AI development and application.
What do you think? As I embark on this project, I’d love to hear your thoughts, suggestions, or any feedback. Do you see other potential applications for Falcon OS? How would you envision using such a system in your own work?
The Journey AheadI aim to document this project publicly, including lessons learned and failures, so that others can recreate and contribute to it. I'll use blog posts to update on progress and set up a GitHub repository. I'm also considering creating a “Falcon OS GPT” (using OpenAI’s service to create specialized GPTs) to assist with specialist questions about the project as it evolves. It could be helpful for myself or others to query a model trained specifically on the project's content.
OpenAI introducing GPTs
Overall, I'm excited to explore Falcon's potential as the foundation for an open ecosystem of tools for complex tasks, with the LLM as the orchestrating kernel. I will focus on crafting prompts and fine-tuning to leverage multiple tools like databases and document repositories to empower Falcon to solve problems. I hope this transparent documentation of my wins, stumbles, and lessons can help advance true open AI development.
Continue Reading About The Falcom OS Project
Falcon OS Part 2: LLM Decision Making with ReAct
In this blog post we explore the concept of LLM decision making by examining the ReAct framework.
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