Exploring open-weights models on Hugging Face | CS 375-376 Spring 2025 at Calvin University

Today we’re going to try out some of the open-weights models that are available on Hugging Face.

Objectives:

I’ve used a high-level API to run a neural network model.
I’ve generated an array of class probabilities.
I’ve computed the similarity between two embeddings and compared that with another pairwise similarity.

Background

Hugging Face is a company that provides a platform for sharing and using pre-trained models. They provide:

A model hub where you can find and download models.
A hosting service called Spaces where people can share their models.
Python libraries for working with models, including:
- transformers for working with models.
- datasets for working with datasets.

Let’s start by playing with a few Spaces, then we’ll try out some models.

Go to the Spaces page. Try out two or three different Spaces:

One that’s highlighted or trending
One that is designed for a task that you might be interested in exploring for a project
One that is designed for a task that you’re not familiar with

A few that I tried out in February 2025:

Explore Sentence Embeddings (name: u08s1-sentence-embeddings.ipynb; show preview, open in Colab) (this one needs Colab; Kaggle doesn’t seem to support the tensorboard viewer that this uses.). Try computing the embeddings for some sentences that you create by hand, and then computing the cosine similarity between them. What do you find? Are similar sentences closer together in the embedding space?
Try the SigLIP demo that embeds images and text together. Try computing the dot products between a few texts that you write by hand. Does the dot product reflect the similarity of the texts? Repeat with images. What do you find? (This one definitely needs Colab.)