There are over 325,000 models on Huggingface 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, and 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 two types of
generative AI models: 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 that 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, one 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 one is pre-trained open
source LLMs allow a process called fine tuning. That means you can add features to the LLM that
benefit your specific use case and the LLMs can be trained on specific data sets. So I can
fine tune an LLM with my own data. And community contributions are a big plus. Using a proprietary
LLM means you're reliant on a single provider, whereas open source models benefit from community
contributions and multiple service providers. You can experiment and use contributions from people
with varying perspectives. And these benefits have led to all sorts of organizations to use
open source LLMs. In another video, I addressed how NASA and IBM developed an open source LLM
trained on geospatial data. Some healthcare organizations use open source LLMs for diagnostic
tools and treatment optimization. There's an open source LLM called FinGPT [fin / financial]. It was
developed for the financial industry. Which brings us onto the topic of talking about some specific
open source LLMs that you might find of interest. Now Huggingface maintains an open LLM leaderboard,
and that tracks , ranks, and evaluates open source LLMs on various benchmarks like which LLM
is scoring highest on the Truthful AI Benchmark series, which measures whether a language model
is truthful in generating answers to questions. So it gives those answers a score. And the top spots
on this leaderboard, they change frequently. And it's quite fun to watch the progress these
models are making. Many of the models on the leaderboard are variations on the Llama 2 open
source LLM. That's the one provided by Meta AI. And Llama 2 encompasses pre-trained and
fine tuned generative text models from 70 billion all the way down to 7 billion parameters. And it's
licensed for commercial use. Vicuna was created on top of the Llama model and fine tuned to follow
instructions. And then it's also Bloom by BigScience, which is a multilingual language model
created by more than 1000 AI researchers. Now, one area that both proprietary and open source LLMs
share is their associated risks. Although LLM outputs often sounds fluent and authoritative,
they can be confidently wrong. Hallucinations, they can result from the LLM being trained on
incomplete, contradictory, or inaccurate data from misunderstanding context. Bias happens
when the source of data is not diverse or not representative. And security problems can include
leaking PII, and cybercriminals using the LLMs for malicious tasks like phishing. Especially in these
early days of large language models, we do need to mitigate risk. But open source LLMs are thriving
in business. Here at IBM, the Watsonx.ai Studio makes available access to multiple Llama 2 models,
and IBM has released a series of foundation models of its own called Granite. And this
space is changing rapidly, making open source LLMs a field well-worth keeping a close eye on.