Beyond Licensing Fees: How Open Source LLMs are Democratizing AI

Large language models (LLMs) are the cornerstone of the current generative AI revolution. Built on transformer architecture, a powerful neural network design, LLMs are foundation models, like ChatGPT and Gemini, that leverage artificial intelligence, deep learning and massive datasets to produce human-like content. Based on accessibility and ownership, there are two types of large language models: proprietary large language models and open source large language models.

Proprietary or closed source LLMs are developed and controlled by a company, requiring customers to purchase a license to use the model. The license places restrictions on how customers can use LLM, and they have limited information about how the technology works.

Open source LLMs, on the other hand, are free and available for the public to use, modify and customize. In other words, any individual or business can use, or even fine-tune, open source LLMs for their requirements without having to purchase a license.

What are the Benefits of Open Source Large Language Models

The term ‘open source’ refers to a practice in software development where the source code and underlying architecture used to build and train the model is accessible to the public, meaning anyone can access, improve, and modify the model. This openness fosters collaboration, innovation, flexibility, and transparency.

There are multiple — short-term and long-term — benefits of open source LLMs, including transparency, flexibility, added features, etc.

Transparency

The transparent nature of open-source LLMs allows users to understand the mechanisms behind the technology, including their architecture, methodologies, and functionality. This democratization of generative AI technology builds trust and helps in informed decision-making as companies have visibility into algorithms allowing them to ensure ethical and legal compliance, as well as better understand the limitations of the model.

Enhanced Data Security and Privacy

Businesses lacking in-house data science or machine learning expertise can deploy open-source LLMs on their own servers or in the cloud. Consequently, they can have full control over their data and prevent the exposure of sensitive information through external access. This reduces the risk of data leaks and unauthorized access.

In a nutshell, companies without internal machine learning expertise can benefit from open source LLMs without compromising their data and sensitive information.

Cost Savings

Open source LLMs cost much less in the long run compared to closed source LLMs because of the absence of licensing fees. This is particularly advantageous for businesses with budget considerations, including small organizations. However, operating LLMs involves infrastructure costs, whether on-premises or in the cloud.

Added Features and Customization

Pre-trained on a vast amount of data, large language models can be fine-tuned to suit the specific needs of businesses. Open source LLMs lower entry barriers for enterprises by offering access to the inner workings. This means developers can add features or train the model on industry specific datasets to meet their requirements. In the case of closed source LLMs, making such changes involves working with a vendor, which needs time and money.

Top 5 Open-Source Large Language Models

LLaMa 2

Released by Meta AI in 2023, LLaMa 2 is a powerful, open source large language model trained on 2 trillion tokens supporting between 7 to 70 billion parameters. It is free of charge for research and commercial use and can execute various natural language processing (NLP) tasks, such as text and programming code generation. In addition to its open-source principles, LLaMa focuses on developing and improving the performance capabilities of smaller models.

Mistral: Best 7B Pre-trained Model

Mistral 7B is a small, high-performance open source large language model with 7 billion parameters. It leverages 7B techniques, like group-query attention, for quicker predictions or decisions based on data analysis, and sliding window attention (SWA) to deal with long sequences of queries, such as articles.

These technologies empower Mistral to process and create large texts faster and at a lower cost compared to more resource-intensive language models.

BERT

Launched by Google in 2018, Bidirectional Encoder Representations from Transformers (BERT) is an open source deep learning model for natural language processing. Introduced with innovative features in the early days of LLM, BERT is one of the most popular and widely used open source language models with state-of-the-art performance in many natural language processing tasks.

Anyone can use pre-trained BERT models to build applications faster. According to Google, BERT enables users to train a cutting-edge question-and-answer system in just 30 minutes on a cloud TPU and in a few hours using a GPU.

Falcon 180B

Falcon 180B was launched by the Technology Innovation Institute of the United Arab Emirates in September 2023. One of the biggest open source LLMs, Falcon 180B was trained on 180 billion parameters and 3.5 trillion tokens and already outperformed other models like LLaMa 2 and GPT-3.5 on various tasks related to natural language processing (NLP).

While Falcon 180B is free for commercial and research purposes, leveraging its capabilities requires powerful computing resources.

Code LLaMa

Built on LLaMa 2, Code LLaMa from Meta is an AI model specifically designed for code-related tasks. It was trained on 500 billion code tokens and code-related data to help programmers with code generation or understanding existing code.

Code LLaMa has a varying number of parameters — 7B, 13B, and 34B — and has been fine-tuned to generate code and explain code functionality in a diverse set of programming languages, including C++, Java, Python, PHP, Javascript, C#, Bash, and many more.

Code LLaMa Python and Code LLaMa Instruct are two primary variations of Code LLaMa. Trained on an additional 100B tokens of Python code, Code LLaMa Python brings enhanced code creation capabilities in the Python programming language. Code LLaMa Instruct is a fine-tuned version of Code LLaMa to better understand human instructions.

These are some of the top open source large language models being developed and fine-tuned, to be accessible to the public. The proliferation of these models illustrates the requirement for open AI solutions is growing rapidly.

An array of organization types leverage open source LLMs to analyze, identify, and summarize information without sharing proprietary data outside the organization. For example, many news publishers are exploring using LLMs to generate news content. Likewise healthcare organizations are also researching and experimenting with LLMs for applications, including treatment optimizations, diagnosis tools, and handling patient information. With more iterations of these models, the utility of these applications will continue to expand.