Image Feature Extraction
Transformers
Safetensors
motif_vision
feature-extraction
motif
vision-transformer
self-supervised
video
custom_code
Instructions to use Motif-Technologies/Motif-Vision-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Motif-Technologies/Motif-Vision-Encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="Motif-Technologies/Motif-Vision-Encoder", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Motif-Technologies/Motif-Vision-Encoder", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
docs(README): add architecture figure to Model details
Browse filesAdds an architecture diagram (image/video inputs, patch embedding, 40-block transformer stack, and a transformer-block internals callout) at the top of the Model details section, pairing the visual with the spec table. Figure is deterministic matplotlib output at 2791px width.
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## Model details
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| Backbone | ViT-7B, patch 16, embed 4096, depth 40, heads 32, SwiGLU |
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## Model details
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<p align="center">
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<img src="assets/architecture.png" width="820" alt="Motif Vision Encoder architecture: image and video inputs, patch embedding, 40-block transformer stack, and transformer block internals"/>
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</p>
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| Backbone | ViT-7B, patch 16, embed 4096, depth 40, heads 32, SwiGLU |
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assets/architecture.png
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