The author suggests starting with APIs when integrating LLMs into a system or business, then reproducing results with open-source models, and finally testing out hardware needs. They emphasize that while creating an architecture that scales on OpenAI level with large models is challenging, most businesses need to focus on solving a useful problem for their customers and ensuring the AI solution works reliably.
Key takeaways:
- Large Language Models (LLMs) are trained on vast amounts of textual data and can predict potential next words in a sequence. The size of these models is measured by the number of parameters they have.
- Open source models like Llama 2 and Falcon are available for commercial use and can be run using libraries like HuggingFaces. These models come in various sizes and can be used depending on the specific use case.
- Running these models requires hardware that can handle the model size and speed requirements. Smaller models can run on CPUs, while larger models require GPUs.
- Integrating LLMs into a system or business involves starting with APIs, reproducing results with open source models, and testing hardware needs. It's important to validate that AI can provide value to the business and that the chosen models are effective.