The move by the Linux Foundation to include the Open Model Initiative (OMI) might lead to the development of “more ethical” large language models (LLMs), according to experts.

“The primary goal for OMI being part of the Linux Foundation is to foster ethical practices in the use of data (text/images) for training generative AI models,” stated Abhigyan Malik, who leads data, analytics, and AI at Everest Group.

However, Malik cautioned that ensuring the ethical sourcing of training data is becoming more challenging due to increased awareness of data protection and changes in privacy and usage policies by major data providers.

Currently, several leading LLM providers, including Open AI and Stability AI, are involved in legal disputes over accusations of copyright infringements during their model training processes.

The Open Model Initiative (OMI), initiated in June by three startups—Invoke, Civitai, and Comfy Org, establishes a platform for developers, researchers, and businesses to collaborate on the development of open and permissively licensed AI models.

As outlined by the Linux Foundation, permissive licenses facilitate easy participation and sharing among community members, eliminating the burden of downstream obligations.

“This approach is particularly advantageous in software sectors requiring the distribution of proprietary software based on an open source framework, while keeping the modifications private,” stated the Foundation in one of its guides on open source software.

The primary goal of OMI is to harness collective expertise in model training and inferencing to create models that are comparable or superior to proprietary options, such as large language models (LLMs) produced by prominent players like OpenAI, Google, and AWS, but without the restrictive licensing terms that limit model utilization.

The OMI, set to be managed by a community-based steering committee, will put in place a governance framework and establish working groups to promote cooperative efforts within the community.

This initiative will also carry out a survey to collect insights on future model research and training needs, as announced by the Linux Foundation. The initiative aims to develop unified standards that will improve model interoperability and metadata handling.

Moreover, the OMI plans to build a transparent dataset for training purposes and will develop an alpha test model specifically for red teaming exercises.

As outlined by the Foundation, the key objective for this initiative is to release an alpha version of the model complete with fine-tuning scripts to the community by year’s end.

The significance of this move for enterprises lies in the unavailability of source code and the license restrictions from LLM-providers such as Meta, Mistral and Anthropic, who put caveats in the usage policies of their “open source” models.

Meta, for instance, according to Everest Group’s other AI practice leader Suseel Menon, does provide the rights to use Llama models royalty free without any license, but does not provide the source code.

“Meta also adds a clause: ‘If, on the Meta Llama 3, monthly active users of the products or services is greater than 700 million monthly active users, you must request a license from Meta.’ This clause, combined with the unavailability of the source code, raises the question if the term open source should apply to Llama’s family of models,” Menon explained.

In contrast, OMI’s objective, according to analysts, is to create models that don’t present enterprises with caveats and are more freely accessible.

OMI’s objectives and vision received mixed reactions from analysts.

While Amalgam Insights’ chief analyst Hyoun Park believes that OMI will lead to the development of more predictable and consistent standards for open source models, so that these models can potentially work with each other more easily, Everest Group’s Malik believes that OMI may not be able to stand before the might of vendors such as Meta and Anthropic.

“Developing LLMs is highly compute intensive and has cost big tech giants and start-ups billions in capital expenditure to achieve the scale they currently have with their open-source and proprietary LLMs,” Malik said, adding that this could be a major challenge for community-based LLMs.

The AI practice leader also pointed out that previous attempts at a community-based LLM have also not garnered much adoption, as models developed by larger entities tend to perform better on most metrics.

“BLOOM serves as a clear example of an open LLM that, while successful in fostering a community-oriented model, has yet to achieve widespread adoption due to certain inefficiencies and specific design decisions, such as its intentional avoidance of a chatbot interface,” Malik stated.

Nevertheless, the AI practice leader mentioned that OMI has potential to carve out specific roles in the content creation industry, including tasks related to 2D/3D imagery, graphical design, and editing.

“These specialized areas, like 3D image creation or catalog image editing for commercial use, match well with the capabilities of OMI’s models,” Malik commented.

According to Malik, this approach seems plausible, especially considering platforms like Invoke, which caters to professional studios, and Civitai, designed for creative users.

One of the other use cases for OMI’s community LLMs is to see their use as small language models (SLMs), which can offer specific functionality at high effectiveness or functionality that is restricted to unique applications or use cases, analysts said.

Currently, OMI’s GitHub page has three repositories, all under Apache 2.0 license.

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