Top Open-Source Generative AI Models Revolutionizing the Industry
The Best Open-Source Generative AI Models Available Today
There are many reasons that businesses may want to choose open-source over proprietary tools when getting started with generative AI. This could be because of cost, opportunities for customization and optimization, transparency, or simply the support that’s offered by the community.
With software generally, the term open-source simply means that the source code is publicly available and can be used, free of charge, for pretty much any purpose. When it comes to AI models, though, there has been some debate about exactly what this entails, as we will get into it as we discuss the individual models covered here. So, let’s dive in.
One of the most powerful and flexible image generation models, and certainly the most widely-used open-source image models, Stable Diffusion 3 (the latest version as of writing) supports text-to-image as well as image-to-image generation and has become well-known for its ability to create highly realistic and detailed images. As is common with open-source software, using Stable Diffusion isn’t quite as straightforward as using commercial, proprietary tools like ChatGPT. Rather than having its own web interface, it’s accessed through third-party tools built by commercial entities, including DreamStudio and Stable Diffusion Web. The alternative is to compile and run it yourself locally, and this requires providing your own compute resources as well as technical know-how.
Meta AI is another powerful option, a family of language models available in various sizes, making it suitable for different applications. One of its strong points is its ability to run on relatively low-powered hardware. However, there is some debate as to whether it can truly be considered open-source, as Meta has not disclosed exact details of its training data.
Mistral, an open-source generative AI model developed by a French startup, is available in different versions to suit varying needs from lightweight deployment to more powerful capabilities. Mistral has a strong user community offering support and positions itself as a highly flexible and customizable generative language model.
OpenAI has open-sourced the second version of their LLM, which can be used for various language-based tasks. While it isn’t as big or powerful as later versions, it is still considered to be adequate for many applications. OpenAI has made GPT-2 available under the MIT license, which is generally considered compliant with open-source principles.
BLOOM, described as the world’s largest open, multilingual language model built on 176 billion parameters, was a collaborative effort involving over 1,000 researchers from the Hugging Face repository. While technically not completely open-source, it is freely available for non-harmful purposes as defined by the project’s Responsible AI License, making it an interesting experiment in developing ethical AI.
Grok, designed and built by X.ai, is another open-source generative AI model that has drawn some skepticism regarding its open-source status due to the lack of full code disclosure. The models released by the Technology Innovation Institute in Abu Dhabi, Falcon 40B, and 180B, have been received positively and are considered among the top LLMs on Open Face’s performance leaderboard.
In conclusion, the realm of open-source generative AI tools offers a diverse array of options that hold transformative potential for businesses eager to leverage AI’s power while embracing transparency, cost-efficiency, and robust community support. Each of these models has its strengths and weaknesses, and businesses should carefully consider their specific needs and goals when choosing the right generative AI model for their projects.