SmolVLM2

SmolVLM is a new vision language model made by Hugging Face. It is designed to be efficient and lightweight, making it great for different devices, from smartphones to servers. This model comes in various sizes to fit different needs and hardware capabilities.

Key Features

SmolVLM stands out because it is efficient and easy to use. It works well on laptops, consumer GPUs, and even mobile devices without losing performance. The model''s small memory footprint allows it to handle tasks that were previously impossible on such devices.

The model has several key modifications.
It has a new language backbone that replaces Llama 3.1 8B with SmolLM2 1.7B. It compresses visual information using a pixel shuffle strategy. It has a shape optimized SigLIP vision backbone with patches of 384x384 pixels and inner patches of 14x14.

These changes result in a model that encodes each 384x384 image patch to 81 tokens, significantly reducing memory consumption and increasing throughput.

Benefits

SmolVLM performs better than other models in its class, especially in video understanding tasks. It shows strong performance, especially in the 2B range, and can run in a free Google Colab environment.

Use Cases

SmolVLM2 2.2B

The 2.2B parameter model is the main version. It offers strong performance across various vision and video tasks. It can solve math problems with images, read text in photos, understand complex diagrams, and tackle scientific visual questions.

SmolVLM2 500M

This model offers video capabilities close to the 2.2B version but at a fraction of the size. It is designed for scenarios where memory and computational resources are limited.

SmolVLM2 256M

The smallest model in the family, SmolVLM2 256M, pushes the boundaries of what is possible with ultra-small models. It is experimental but shows promise for specialized fine-tuning projects and creative applications.

Practical Applications

iPhone Video Understanding

An iPhone app runs SmolVLM2 locally, allowing users to analyze and understand video content directly on their device without needing cloud support.

VLC Media Player Integration

In collaboration with VLC media player, SmolVLM2 provides intelligent video segment descriptions and navigation, enabling users to search through video content semantically.

Video Highlight Generator

Available as a Hugging Face Space, this application automatically extracts the most significant moments from long-form videos, making it a powerful tool for content summarization.

Cost/Price

The cost of SmolVLM2 will depend on the specific variant and the platform it is used on. For more information, visit the Hugging Face SmolVLM page, https://huggingface.co/collections/HuggingFaceTB/smolvlm2-smallest-video-lm-ever-67ab6b5e84bf8aaa60cb17c7.

Funding

SmolVLM was trained using a mixture of long and short context datasets, including books from Project Gutenberg and code documents from The Stack. The model was fine-tuned to extend its context window to 16k tokens, making it suitable for multiple images and long videos.

Reviews/Testimonials

SmolVLM is noted for its efficiency, flexibility, and robust performance, making it an ideal choice for developers and researchers working with constrained devices or looking to cut inference costs. Its open-source nature and comprehensive toolkit have received positive feedback, highlighting its potential to revolutionize the field of multimodal AI.