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Matryoshka Diffusion Models
Paper β’ 2310.15111 β’ Published β’ 42 -
AToM: Amortized Text-to-Mesh using 2D Diffusion
Paper β’ 2402.00867 β’ Published β’ 11 -
Neural Network Diffusion
Paper β’ 2402.13144 β’ Published β’ 95 -
Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers
Paper β’ 2402.19479 β’ Published β’ 34
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π’ Our EMOVA paper has been accepted by CVPR 2025, and we are glad to release all resources, including code (training & inference), datasets (training & evaluation), and checkpoints (EMOVA-3B/7B/72B)!
π€ EMOVA is a novel end-to-end omni-modal LLM that can see, hear and speak. Given omni-modal (i.e., textual, visual and speech) inputs, EMOVA can generate both textual and speech responses with vivid emotional controls by utilizing the speech decoder and a style controller.
β¨ EMOVA Highlights
β
State-of-the-art omni-modality: EMOVA achieves SoTA comparable results on both vision-language and speech benchmarks simultaneously.
β
Device adaptation: our codebase supports training/inference on both NVIDIA GPUs (e.g., A800 & H20) and Ascend NPUs (e.g., 910B3)!
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Modular design: we integrate multiple implementations of vision encoder, vision projector, and language model, even including the most recent DeepSeekMoE-tiny!
π₯ You are all welcome to try and star!
- Project page: https://emova-ollm.github.io/
- Github: https://github.com/emova-ollm/EMOVA
- Demo: https://huggingface.co/spaces/Emova-ollm/EMOVA-demo
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mexicanamerican/DeepSeek-R1-1.5B-Medical-COT-Q8_0-GGUF
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