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Developers' Guide to Unlocking the Power of Open Source LLMs
For developers seeking greater control and customization over their large language models when working on genAI applications, here are reasons why open source LLMs are a better choice than proprietary ones.
4 Min Read
Alamy
Want to take advantage of large language models (LLMs) but don't want to depend on a proprietary AI service that limits your control over the configuration and data behind your model?
If so, an open source LLM may be exactly what you're looking for. While open source LLMs can have some distinct drawbacks, they're an important solution for developers seeking capabilities that proprietary LLMs just can't deliver.
Keep reading for a breakdown of what open source LLMs are, how they work, and why developers should or shouldn't consider taking advantage of them when working on generative AI applications.
What Is an Open Source LLM?
An open source large language model is an AI model that can be freely used and customized because its source code is available for anyone to inspect and modify.
Open source LLMs are distinct from the LLMs developed by companies such as OpenAI, which does not release the source code of its models or disclose much about how they work internally. Most of the commercial generative AI solutions that have appeared to date depend on proprietary LLMs, but an increasing number of open source LLMs are available.
Where Can You Find Open Source LLMs?
Popular open source LLMs available today include:
Llama 2, an open source LLM developed by Meta.
Bloom, an LLM created via a large collaborative project without oversight by any large company or organization.
Falcon, another LLM developed with the goal of limiting the direct influence of major corporations.
Mistral-7B, an open source LLM developed by the French company Mistral AI, which has positioned itself as a major advocate of open source approaches to generative AI development.
How Do Open Source LLMs Work?
Most open source LLMs work by providing the public with access to source code, which developers can download and use to deploy an LLM that powers an AI service or app. If they wish, they can customize the model by changing its source code or modifying parameters before deploying it.
Many open source LLMs are also pretrained, which means that they are ready to use without having to perform the compute-intensive process of exposing the model to large volumes of data so that it can identify relevant patterns. However, developers who use an open source LLM can typically retrain the model if they wish to use custom training data.
The Benefits of Open Source LLMs
The main benefit that open source LLMs provide is that — like almost any type of open source software — they give developers more control than they'd get from a proprietary solution. Again, developers have the ability to customize how an open source LLM behaves by modifying its source code or retraining it using data of their choosing.
Open source LLMs can also offer more transparency than proprietary LLMs because the ability to inspect source code makes it easier to understand why an LLM generates particular results. That said, because the organizations that release open source LLMs don't always reveal which datasets they used to train their models, the transparency of open source LLMs may be limited if developers deploy pretrained models. If you don't know which data was used to train your model, you may not know why the model behaves as it does.
Challenges of Using an Open Source LLM
On the other hand, open source LLMs present some challenges — starting with the fact that, on the whole, they are more complicated to configure and deploy than proprietary LLMs.
Building an application that integrates with an LLM from a vendor like OpenAI is typically as simple as implementing some API calls so that the app can interact with the vendor's AI service. To use an open source LLM, however, in most case you need to deploy the model yourself, then connect your app to it.
In addition, customizing open source LLMs requires specialized expertise, especially if you want extensive customizations. It's more complicated than changing some configuration variables or modifying a deployment environment; you need a deep understanding of how the model works and (if you want to use custom training data) how to retrain it.
Conclusion: When, and When Not, to Use Open Source LLMs
For developers looking for the simplest path to taking advantage of generative AI, using a proprietary AI model or service is the best solution.
But if you want more control over and transparency into your model, and you have the skills and patience necessary to manage the model yourself, consider using an open source LLM. The wide selection of open source LLMs available today makes it easy to find an offering tailored to particular needs, and while customizing LLMs is not trivial, it's not an insurmountable challenge for developers already familiar with the basics of AI/ML.
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