This is a first look into Nvidia's NIMs, and how "easy" it becomes to run big deep learning models, including text-to-text: LLMs and the newly released Llama 3.1, Vision, Speech, among others.

I have included a Jupyter notebook llama3_1.ipynb with sample code for LLM reasoning, and on how to call and run inference on Llama 3.1: the smaller yet very powerful 8B version, as well as the massive 405B version. In this notebook, I wrapped the models in simple python functions that one can call by simply running:

llama3_1_8b("quante lingue parli?")

(to which Llama 3.1 said: "Parlo l'italiano, l'inglese, il francese e il tedesco."...)

If you are interested in text-to-image generation, and/or Diffusion models, I have also included a Jupyter notebook with sample code to use stabilityai's Stable-Diffusion-xl, wrapped in an easy-to-use python function. I asked the model to generate a gen_image_diffusion("A very handsome Maine Coon, sharp focus, 4k"), and this is what I got back!

Note: You will need an API key, which you can obtain by following the instructions on Nvidia's API catalog. You will very likely have to install certain python libraries, which would depend on the model you want to run. To run Llama 3.1, you may want to install:

• python-dotenv
• openai