Deploy Open Source Models on watsonx → https://ibm.biz/BdSaSj

Large Language Models (LLMs) can be proprietary to a given company, or open source and free for anyone to access and modify. While proprietary LLMs are often larger, the benefits of transparency, fine-tuning, and community contributions make open source an attractive alternative. Both proprietary and open source LLMs share risks, including inaccuracies, bias, and security concerns. In this video, Master Inventor Martin Keen covers the tradeoffs so you can make an informed decision of which option is best for you.

There are over 325,000 models on Hugging Face and thousands more are being added.

And why might you choose to use AI models like these?

Well, let's start by getting a few things straight.

The models we're talking about in this video they're specifically LLMs, that's large language models, which are foundation models that use artificial intelligence, deep learning, and massive datasets to generate text, we're talking generative AI.

And there are 2 types of generative AI model.

There's proprietary models and there are open source models.

Now proprietary LLMs, those are owned by a company who can control its usage.

A proprietary LLM may include a license that restricts how the LLM can be used.

On the other hand, open source LLMs are free and available for anyone to access.

And developers and researchers are free to use, improve or otherwise modify the model.

Now, look, it's not true in every instance, but generally many proprietary LLMs are far larger in size than open source models and specifically in terms of parameter size.

Some of the leading proprietary LLMs extend to thousands of billions of parameters.

Actually we don't necessarily know because well those LLMs and their parameter counts are proprietary.

But bigger isn't necessarily better and the open source model ecosystem is showing promise in challenging the proprietary LLM business model.

So let's discuss the benefits of open source LLMs.

Let's talk about the types of organizations that are using them.

Let's talk about some of the leading open source models available today.

And we should talk about the risks associated with using them.

Now, clearly 1 of the benefits of a open source large language model that has to be transparency.

Open source LLMs may offer better insight into how they work, their architecture, and the training data used to develop them.

Another big 1 is pre-trained open source LLMs allow a process called fine-tuning.


Speaker 0: There are over 325,000 models on Hugging Face and thousands more are being added. And why might you choose to use AI models like these? Well, let's start by getting a few things straight. The models we're talking about in this video they're specifically LLMs, that's large language models, which are foundation models that use artificial intelligence, deep learning, and massive datasets to generate text, we're talking generative AI. And there are 2 types of generative AI model.

There's proprietary models and there are open source models. Now proprietary LLMs, those are owned by a company who can control its usage. A proprietary LLM may include a license that restricts how the LLM can be used. On the other hand, open source LLMs are free and available for anyone to access. And developers and researchers are free to use, improve or otherwise modify the model.

Now, look, it's not true in every instance, but generally many proprietary LLMs are far larger in size than open source models and specifically in terms of parameter size. Some of the leading proprietary LLMs extend to thousands of billions of parameters. Probably? Actually we don't necessarily know because well those LLMs and their parameter counts are proprietary. But bigger isn't necessarily better and the open source model ecosystem is showing promise in challenging the proprietary LLM business model.

So let's discuss the benefits of open source LLMs. Let's talk about the types of organizations that are using them. Let's talk about some of the leading open source models available today. And we should talk about the risks associated with using them. Now, clearly 1 of the benefits of a open source large language model that has to be transparency.

Open source LLMs may offer better insight into how they work, their architecture, and the training data used to develop them. Another big 1 is pre-trained open source LLMs allow a process called fine-tuning. That means you can...

See all IBM Technology transcripts on Youtube

Should You Use Open Source Large Language Models?