Finally, it is time to dive in and select a suitable LLM for your task: numerous LLMs are available, but many providers, big and small, are rushing to create new LLMs. You have a choice of different LLMs from main cloud platform providers like GCP, MS Azure, AWS, and from open-source alternatives in popular LLM repository sites like Huggingface (https://huggingface.co/).
There are a couple of key aspects to consider when choosing the LLM. The first is the type of LLM. We distinguish between foundational LLMs and more specific LLMs. Foundational LLMs are models designed and trained for all kinds of general text-based generative AI tasks. Specific LLMs are already fine-tuned for particular areas and topics. If your task fits one of the special topics, it’s preferable to assess that one first. The next key aspect to consider is the size of the LLM, typically measured by the number of parameters. Smaller LLMs are usually somewhat less accurate and less applicable in all domains. However, their application means less resources and lower costs. In general, go for the smallest LLM that matches your problem and offers the desired accuracy. This will save you money and effort. Also, you can fine-tune many existing LLMs with your own data, and thus create a customized LLM that best fits your goals.