More than three decades ago, Red Hat saw the potential of open source development and licenses to create better software and foster IT innovation. Thirty million lines of code later, Linux not only developed to become the most successful open source software, but also maintains that position to this day. The commitment to open source principles continues, not only in the corporate business model but also as part of the work culture. In the company's assessment, these concepts have the same impact on artificial intelligence (AI) if done correctly, but the tech world is divided on what would be the "right way."
AI, especially large language models (LLMs) behind generative AI (gen AI), cannot be viewed in the same way as an open program. Unlike software, AI models mainly consist of numerical parameter models that determine how a model processes inputs, as well as the connections it makes between various data points. Trained model parameters are the result of a long process involving vast amounts of training data that are carefully prepared, mixed, and processed.
Although the model parameters are not software, in some aspects they serve a similar function to code. It is easy to compare that the data are the source code of the model, or they would be very close to it. In open source, the source code is commonly defined as the "preferred way" to make modifications to the software. Training data alone do not fit this function, given that their size differs and due to their complex pre-training process, which results in a tenuous and indirect connection between any item of the training data and the trained parameters and the resulting behavior of the model.
Most of the improvements and enhancements in AI models happening now in the community do not involve access to or manipulation of the original training data. Instead, they are the result of modifications to the model parameters or to a process or adjustment that can also be used to tune the model's performance. The freedom to make these improvements to the model requires that the parameters be released with all the permissions that users receive under open source licenses.
Red Hat's vision for open source AI.
Red Hat believes that the foundation of open source AI lies in theopen source licensed model parameters combined with open source software componentsThis is a starting point for open-source AI, but not the final destination of philosophy. Red Hat encourages the open source community, regulatory authorities, and industry to continue striving for greater transparency and alignment with open source development principles by training and fine-tuning AI models.
This is Red Hat's vision as a company, which encompasses an open source software ecosystem, to practically engage with open source AI. It is not a formal attempt at definition, like the one thatOpen Source Initiative(OSI) is developing with itsOpen Source AI Definition(OSAID). This is the corporation's point of view that makes open source AI feasible and accessible to the largest set of communities, organizations, and providers.
This point of view in practice is put into practice through work with open source communities, highlighted by the project.InstructLab, led by Red Hat and the effort with IBM Researchin the Granite family of licensed open source modelsInstructLab significantly reduces the barriers for non-data scientists to contribute to AI models. With InstructLab, domain experts from all sectors can add their skills and knowledge, both for internal use and to help a shared open-source AI model that is widely accessible to upstream communities.
The Granite 3.0 model family handles a wide range of AI use cases, from code generation to natural language processing for extractioninsightslarge datasets, all under a permissive open source license. We helped IBM Research bring the Granite code family to the open source world and continue to support the family of models, both from an open source perspective and as part of our Red Hat AI offering.
The repercussion of therecent announcements from DeepSeekshows how open source innovation can impact AI, both at the model level and beyond. There are obviously concerns about the Chinese platform's approach, mainly that the model's license does not explain how it was produced, which reinforces the need for transparency. With that said, the mentioned disruption reinforces Red Hat's vision of the future of AI: an open future, focused on smaller, optimized, and open models that can be customized for specific enterprise data use cases across any location in the hybrid cloud.
Expanding AI models beyond open source
Red Hat's work in the open source AI space goes far beyond InstructLab and the Granite family of models, extending to the tools and platforms necessary to effectively consume and productively use AI. The company has become very active in promoting technology projects and communities, such as (but not limited to):
● RamaLamaan open-source project aimed at facilitating the local management and deployment of AI models;
● TrustyAI, an open-source toolkit for building more responsible AI workflows;
● Climatik, a project focused on helping to make AI more sustainable in terms of energy consumption;
● Podman AI Laba developer toolkit focused on facilitating experimentation with open source LLMs;
THErecent announcementabout Neural Magic broadens the corporate perspective on AI, making it possible for organizations to align smaller and optimized AI models, including licensed open source systems, with their data, wherever they reside in the hybrid cloud. IT organizations can then use the inference servervLLMto drive the decisions and production of these models, helping to build an AI stack based on transparent technologies and support.
For the corporation, open source AI lives and breathes in the hybrid cloud. The hybrid cloud provides the flexibility needed to choose the best environment for each AI workload, optimizing performance, cost, scale, and security requirements. The platforms, goals, and organization of Red Hat support these efforts, along with industry partners, clients, and the open source community, as open source in artificial intelligence is driven forward.
There is immense potential to expand this open collaboration in the field of AI. Red Hat envisions a future that encompasses transparent work in models, as well as its training. Whether next week or next month (or even sooner, given the rapid evolution of AI), the company and the open community as a whole will continue to support and adopt efforts to democratize and open up the world of AI.
