Large language models (LLMs) are foundation models that use artificial intelligence (AI), deep learning and massive data sets, including websites, articles and books, to generate text, translate between languages and write many types of content. There are two types of these generative AI models: proprietary large language models and open source large language models.

11pxProprietary LLMs are owned by a company and can only be used by customers that purchase a license. The license may restrict how the LLM can be used. On the other hand, open source LLMs are free and available for anyone to access, use for any purpose, modify and distribute.

The term “open source” refers to the LLM code and underlying architecture being accessible to the public, meaning developers and researchers are free to use, improve or otherwise modify the model.

What are the benefits of open source LLMs?

Previously it seemed that the bigger an LLM was, the better, but now enterprises are realizing they can be prohibitively expensive in terms of research and innovation. In response, an open source model ecosystem began showing promise and challenging the LLM business model.

Transparency and flexibility

Enterprises that don’t have in-house machine learning talent can use open source LLMs, which provide transparency and flexibility, within their own infrastructure, whether in the cloud or on premises. That gives them full control over their data and means sensitive information stays within their network. All this reduces the risk of a data leak or unauthorized access.

An open source LLM offers transparency regarding how it works, its architecture and training data and methodologies, and how it’s used. Being able to inspect code and having visibility into algorithms allows an enterprise more trust, assists regarding audits and helps ensure ethical and legal compliance. Additionally, efficiently optimizing an open source LLM can reduce latency and increase performance.

Cost savings

They are generally much less expensive in the long term than proprietary LLMs because no licensing fees are involved. However, the cost of operating an LLM does include the cloud or on-premises infrastructure costs, and they typically involve a significant initial rollout cost.

Added features and community contributions

Pre-trained, open source LLMs allow fine-tuning. Enterprises can add features to the LLM that benefit their specific use, and the LLMs can also be trained on specific datasets. Making these changes or specifications on a proprietary LLM entails working with a vendor and costs time and money.

While proprietary LLMs mean an enterprise must rely on a single provider, an open source one lets the enterprise take advantage of community contributions, multiple service providers and possibly internal teams to handle updates, development, maintenance and support. Open source allows enterprises to experiment and use contributions from people with varying perspectives. That can result in solutions allowing enterprises to stay at the cutting edge of technology. It also gives businesses using open source LLMs more control over their technology and decisions regarding how they use it.

What kinds of organizations use open source LLMs?

A wide range of organization types use open source LLMs. For example, IBM and NASA developed an open source LLM trained on geospatial data to help scientists and their organizations fight climate change.

Publishers and journalists use open source LLMs internally to analyze, identify and summarize information without sharing proprietary data outside the newsroom.

Some healthcare organizations use open source LLMs for healthcare software, including diagnosis tools, treatment optimizations and tools handling patient information, public health and more.

The open source LLM FinGPT was developed specifically for the financial industry.