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Today, we are releasing a new set of pocket-sized multimodal AI models, trained in partnership with Nebius and already available on Hugging Face 🤗 - Matryoshka style multimodal embeddings ranging from 64 to 256 and 768 dimensions 🖼️ - Improved multimodal chat in 1.2B parameters, aligned with Direct Preference Optimization 💬 - ONNX backend, making PyTorch dependency optional for lightning-fast deployments ⚡ This marks our biggest AI release to date, paving the way for real-time multimodal perception and personalized assistants that can run on any device, including Windows, Linux, macOS, iOS, Android, and most wearable and IoT devices. Tuning with Direct Preference Optimization allowed us to grow our "Multi-Modal Evaluation" (MME) perception score from 863 to 1049 for the same baseline model. Unexpectedly significant for us and visible to the naked eye. Avoiding PyTorch dependency allowed us to shrink the image size from over 5 GB to under 500 MB and will shrink further. Training with Matryoshka losses allows you to crop resulting embeddings, dropping up to 92% of the resulting dimensions but often retaining 99% of the original accuracy. The new guide also covers the recommended quantization and down-casting approaches, helping you export `f32`, `f16`, `i8`, and binary vectors and use them in conjunction with USearch and the rest of our hardware-friendly ecosystem of Search & AI infra 🤗 New models: https://lnkd.in/dpfGcV59 GitHub repository: https://lnkd.in/dTrZ5Q2d

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Leandro Salvador

AI Solutions Manager at Nebius AI I Cloud Infrastructure for Large-Scale ML Workloads

3mo

Thank you for the partnership Unum !

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