Rapidly arrange LLM APIs with BentoML and Runpod
I typically see knowledge scientists getting within the improvement of LLMs when it comes to mannequin structure, coaching strategies or knowledge assortment. Nevertheless, I’ve seen that many instances, exterior the theoretical facet, in many individuals have issues in serving these fashions in a manner that they’ll truly be utilized by customers.
On this transient tutorial, I assumed I might present in a quite simple manner how one can serve an LLM, particularly llama-3, utilizing BentoML.
BentoML is an end-to-end resolution for machine studying mannequin serving. It facilitates Knowledge Science groups to develop production-ready mannequin serving endpoints, with DevOps greatest practices and efficiency optimization at each stage.
We’d like GPU
As in Deep Studying having the correct {hardware} obtainable is vital. Particularly for very massive fashions like LLMs, this turns into much more essential. Sadly, I don’t have any GPU 😔
That’s why I depend on exterior suppliers, so I lease one in all their machines and work there. I selected for this text to work on Runpod as a result of I do know their providers and I feel it’s an reasonably priced worth to observe this tutorial. However if in case you have GPUs obtainable or wish to…
