Large Language Models (LLMs) have revolutionized natural language processing and AI. They are trained on enormous amounts of text data and can generate text, answer questions, and perform other tasks. When it comes to implementing LLMs for internal AI projects, one important decision to make is whether to use open or closed source LLMs. In this post, we’ll explore the benefits and disadvantages of both options.

Open Source LLMs

Open Source LLMs are language models whose source code is publicly available and can be freely accessed, used, modified, and distributed by anyone.

Benefits of Open Source LLMs

• Control: With open-source LLMs, you have control over the model, its training data, and its applications.
• Customization: Open-source LLMs are easier to run and customize because their underlying architecture and weights are publicly available.
• Community support: Open-source LLMs are often supported by a large community of developers who contribute to their development and improvement.
• Innovation: The open-source community is known for its innovation and ability to quickly adapt to new technologies.
• Transparency: With open-source LLMs, you have full visibility into the model’s inner workings, which can help build trust with your customers.

Disadvantages of Open Source LLMs

• Limited resources: Open-source projects may have limited resources compared to closed-source projects backed by large corporations.
• Dependency on community: The development and improvement of open-source LLMs depend on the contributions of the community, which may not always be reliable.

Some popular open source LLMs include Alpaca1, BLOOM2, BLOOMChat2, Cerebras-GPT2, Dolly2, Falcon2, FastChat2, FLAN-T52, FLAN-UL22, GPT-J2, GPT4All2 and GPTNeo2.

Closed Source LLMs

Closed Source LLMs are language models whose source code is not publicly available. They are often developed by large corporations and may be proprietary.

Benefits of Closed Source LLMs

• Resources: Closed-source LLMs are often backed by large corporations with significant resources for development and improvement.
• Support: Closed-source LLMs may come with dedicated support from the company that developed them.

Disadvantages of Closed Source LLMs

• Limited control: With closed-source LLMs, you have limited control over the model, its training data, and its applications.
• Limited customization: The underlying architecture and weights of closed-source LLMs are not publicly available, making customization and fine-tuning impossible.
• Lack of transparency: With closed-source LLMs, you have limited visibility into the model’s inner workings.

Some popular closed source LLMs include Bard, ChatGPT3, Claude, Cohere and Jurassic.

In conclusion, the decision to use open or closed source LLMs for internal AI projects depends on your specific needs and priorities. Open source LLMs offer control, customization, community support, innovation, and transparency. On the other hand, closed source LLMs may have more resources and dedicated support. It’s important to carefully weigh the benefits and disadvantages of both options before making a decision.

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