I typically see knowledge scientists getting within the growth of LLMs by way of mannequin structure, coaching methods or knowledge assortment. Nonetheless, I’ve observed that many instances, outdoors the theoretical side, in many individuals have issues in serving these fashions in a manner that they’ll truly be utilized by customers.
On this temporary 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 answer 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 you already know in Deep Studying having the precise {hardware} out there is essential. Particularly for very massive fashions like LLMs, this turns into much more necessary. Sadly, I don’t have any GPU 😔
That’s why I depend on exterior suppliers, so I lease one in every 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 feel it’s an reasonably priced worth to comply with this tutorial. However when you’ve got GPUs out there or need to…