Video-Text-to-Text
Transformers
Safetensors
English
Chinese
mllama
text-generation
multimodal
video
vision-language
custom_code
text-generation-inference
Instructions to use OpenMOSS-Team/moss-video-preview-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMOSS-Team/moss-video-preview-base with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForCausalLM processor = AutoProcessor.from_pretrained("OpenMOSS-Team/moss-video-preview-base", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("OpenMOSS-Team/moss-video-preview-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload moss-video-preview-base
Browse files
README.md
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@@ -41,7 +41,7 @@ This repo contains the **pretrained weights** that are intended to serve as the
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<img src="assets/model_structure.png" width="90%" alt="Model Architecture"/>
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- **Native
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- **Cross-Modal Projector**: Powered by the proprietary `VideoMllamaTextCrossAttention` mechanism, it achieves high-efficiency semantic alignment between temporal visual features and linguistic context.
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- **Unified Spatio-Temporal Encoding**: Aligns video frame sequences with text tokens, providing a robust backbone for long-context multimodal reasoning.
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<img src="assets/model_structure.png" width="90%" alt="Model Architecture"/>
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</p>
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- **Native Unified Design**: Unlike traditional projection-based models, this architecture provides native, unified support for both image and video streams, ensuring seamless temporal consistency and visual-language decoupling.
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- **Cross-Modal Projector**: Powered by the proprietary `VideoMllamaTextCrossAttention` mechanism, it achieves high-efficiency semantic alignment between temporal visual features and linguistic context.
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- **Unified Spatio-Temporal Encoding**: Aligns video frame sequences with text tokens, providing a robust backbone for long-context multimodal reasoning.
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