Try the Model

Use the widget below to experiment with SmolVLM2. You can detect COCO classes such as people, vehicles, animals, household items.

Overview

SmolVLM2, developed by engineers on the Hugging Face TB Research team, is part of the “Smol Models” initiative. This initiative is focused on making “efficient and lightweight AI models [...] that can run effectively on-device while maintaining strong performance.” SmolVLM2 is capable of both image and video understanding.

SmolVLM2 comes in three sizes: 256M, 500M, and 2.2B. The larger the model, the better performance you can expect on your tasks. When compared with the first SmolVLM model released in November 2024, SmolVLM outperformed on the Mathvista, OCRBench, AI2D, and ScienceQA benchmarks:

According to Hugging Face, the SmolVLM2 models “outperform any existing models per memory consumption. Looking at Video-MME (the go-to scientific benchmark in video), SmolVLM2 joins frontier model families on the 2B range and we lead the pack in the even smaller space.”

We tested SmolVLM on various multimodal tasks. Here were our findings in comparison to other multimodal models available at the time of the model's release:

SmolVLM2 License

SmolVLM2

is licensed under a

Apache 2.0

license.

Performance

Deploy a SmolVLM2 API

You can use Roboflow Inference to deploy a

SmolVLM2

API on your hardware. You can deploy the model on CPU (i.e. Raspberry Pi, AI PCs) and GPU devices (i.e. NVIDIA Jetson, NVIDIA T4).

Below are instructions on how to deploy your own model API.

Label Data Automatically with SmolVLM2

You can automatically label a dataset using SmolVLM2 with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use SmolVLM2 to train a computer vision model.

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