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Molmo 2 doesn't just answer questions about clips—it searches & points. The model returns coordinates & timestamps over videos + images, powering QA, counting, dense captioning, artifact detection, & subtitle-aware analysis. You can see exactly how it reasoned.

68,269 次观看 • 10 个月前 •via X (Twitter)

9 条评论

Ai2 的头像
Ai210 个月前

Last year Molmo set SOTA on image benchmarks + pioneered image pointing. Millions of downloads later, Molmo 2 brings Molmo’s grounded multimodal capabilities to video 🎥—and leads many open models on challenging industry video benchmarks. 🧵

Ai2 的头像
Ai210 个月前

We’re releasing three Molmo 2 variants that run at 2 FPS/8 FPS, depending on your performance needs: 🔹 Molmo 2 (8B): Qwen 3 backbone, best overall grounding + QA 🔹 Molmo 2 (4B): Qwen 3 backbone, fast + efficient 🔹 Molmo 2-O (7B): Olmo backbone, fully open model flow

Ai2 的头像
Ai210 个月前

🎯 Demo: counting objects & actions Show Molmo 2 a clip and ask “How many times does the ball hit the ground?” It returns a count plus space–time pointers (pixel coordinates + timestamps) for each event, so you can verify every answer.

Ai2 的头像
Ai210 个月前

❓ Demo: ask-it-anything video QA Show Molmo 2 a long clip and ask a complex question – “Why does the player change strategy here?” It reasons over the whole timeline & can point to the exact moments that support its answer.

Ai2 的头像
Ai210 个月前

📍 Demo: object tracking Ask Molmo 2 to follow an object—“Follow the red race car.” It places a pink dot on the object and tracks it across frames, returning coordinates over time that you can plug into your workflow.

Ai2 的头像
Ai210 个月前

For Molmo 2, we've significantly upgraded the Ai2 Playground. 🛠️ You can now upload a video or multiple images to try out video summarization, tracking, & counting—giving you a clear view of exactly where the model is looking.

Ai2 的头像
Ai210 个月前

Molmo 2: open, customizable video + image understanding. ▶️ Try in the Playground: ⬇️ Download: 📝 Blog: 📑 Report: 💻 API coming soon

Sanskar Pandey 的头像
Sanskar Pandey10 个月前

def taking this for a spin

Dr. Lmfao 的头像
Dr. Lmfao10 个月前

Providing the spatial-temporal index of the output is not the same as illuminating the opaque inductive logic that generated it.

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Jev is now multimodal. It can see. Here’s how it works and what you can build with it: Jev is a decision model. You give it information, ask questions, and get answers your code can use directly. Previously, that information was text or JSON. Now it can include images. Start with how a Jev request works. You send a “state,” which is simply the information you want Jev to examine. Then you ask questions about it. Each question specifies the kind of answer you need. → Choice picks from options you define, such as “thumbs up” or “fist.” → Noul returns the probability that something is true, such as whether a hand is visible. → Score rates something on a scale you define, such as how open the hand is. With multimodal Jev, images can be part of both the state and the questions. Put a camera frame in the state, and your questions can ask about what’s visible in it. Put a reference photo in a question’s instructions, and you can ask whether an object in the camera frame matches the one in that photo. Jev can now also identify an object and locate it for you. The new Box type brings a YOLO-like capability to Jev. Describe what you want to find, such as “the hand in this image,” and it returns a bounding box around it. Your app can use those coordinates to highlight the object or crop it. One detail matters here. Box returns coordinates even when the target is absent, so pair it with a Noul presence check before using the result. My demo asks about presence, gesture, and location together. Jev answers these independent questions in parallel, and the app combines the results. These capabilities open up prototypes for gesture controls, locating buttons in screenshots, and checking product photos against visual references. Jev’s focus on fast decisions also makes it promising for interactive applications where response time matters, with image latency worth testing in your own workflow. Images and Box are still experimental and require the images “danger zone” header. I cover how it works, the API, and the demo app in the detailed video below. Chapters 0:00 Intro 0:36 Live demo 1:41 What's covered 2:22 How Jev works 3:06 Question types 3:57 Playground demo 6:59 Demo app walkthrough 10:30 Using Multiple images 11:25 Parallel questions 12:40 Danger zone headers 13:07 Outro If you’re new to Jev and want to understand how decision models work, I’ve written a detailed article about it. It’s quoted below.

Akshay 🚀

25,344 次观看 • 8 天前