SmolVLM2, from HuggingFace, is a series of tiny, open-source multimodal model for video understanding. Processes video, images, and text. Ideal for on-device applications.
Sharing SmolVLM2, a new open-source multimodal model series from Hugging Face that's surprisingly small, with the smallest version at only 256M parameters! It's designed specifically for video understanding, opening up interesting possibilities for on-device AI.
What's cool about it:
📹 Video Understanding: Designed specifically for analyzing video content, not just images. 🤏 Tiny Size: The smallest version is only 256M parameters, meaning it can potentially run on devices with limited resources. 🖼️ Multimodal: Handles video, images, and text, and you can even interleave them in your prompts. 👐 Open Source: Apache 2.0 license. 🤗 Hugging Face Transformers: Easy to use with the transformers library.
It's based on Idefics3 and supports tasks like video captioning, visual question answering, and even story telling from visual content.
You can try a video highlight generation demo here.
VLMs this small could run on our personal phones, and many other devices like glasses. That's the future.
SmoLVm2 makes learning languages fun and interactive. 🌐 Loving the progress I'm making! If you vibe with language apps, hit like. Happy learning!
Thanks for sharing SmolVLM2 with the community! It's fascinating how the field of video understanding has evolved, opening doors for more accessible AI applications on personal devices.
Congrats on the launch! Best wishes and sending lots of wins :)
