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 :)
About SmolVLM2 on Product Hunt
“Smallest Video LM Ever from HuggingFace”
SmolVLM2 launched on Product Hunt on March 3rd, 2025 and earned 209 upvotes and 7 comments, placing #7 on the daily leaderboard. 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.
SmolVLM2 was featured in Open Source (68.3k followers), Artificial Intelligence (466.2k followers) and Video (1.8k followers) on Product Hunt. Together, these topics include over 101.8k products, making this a competitive space to launch in.
Who hunted SmolVLM2?
SmolVLM2 was hunted by Zac Zuo. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how SmolVLM2 stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Zac Zuo
Hi everyone！
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.