Rapidly arrange LLM APIs with BentoML and Runpod
I usually see information scientists getting within the improvement of LLMs by way of mannequin structure, coaching strategies or information assortment. Nevertheless, I’ve seen that many instances, outdoors the theoretical facet, in many individuals have issues in serving these fashions in a method that they will truly be utilized by customers.
On this transient tutorial, I assumed I’d present in a quite simple method how one can serve an LLM, particularly llama-3, utilizing BentoML.
BentoML is an end-to-end answer for machine studying mannequin serving. It facilitates Knowledge Science groups to develop production-ready mannequin serving endpoints, with DevOps finest practices and efficiency optimization at each stage.
We’d like GPU
As you recognize in Deep Studying having the proper {hardware} out there is essential. Particularly for very giant fashions like LLMs, this turns into much more vital. Sadly, I don’t have any GPU 😔
That’s why I depend on exterior suppliers, so I lease considered one of their machines and work there. I selected for this text to work on Runpod as a result of I do know their companies and I believe it’s an inexpensive value to observe this tutorial. However in case you have GPUs out there or wish to…
