Instructions to use immich-app/ViT-L-16-SigLIP-256__webli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use immich-app/ViT-L-16-SigLIP-256__webli with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("immich-app/ViT-L-16-SigLIP-256__webli", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- .gitattributes +6 -0
- README.md +1 -2
- textual/model.onnx +2 -2
- textual/rknpu/rk3566/model.rknn +3 -0
- textual/rknpu/rk3576/model.rknn +3 -0
- textual/rknpu/rk3588/model.rknn +3 -0
- textual/tokenizer.json +4 -2
- textual/tokenizer_config.json +3 -1
- visual/model.onnx +2 -2
- visual/rknpu/rk3566/model.rknn +3 -0
- visual/rknpu/rk3576/model.rknn +3 -0
- visual/rknpu/rk3588/model.rknn +3 -0
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README.md
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---
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# Model Description
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This repo contains ONNX exports for the
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Visual and textual encoders are separated into separate models for the purpose of generating image and text embeddings.
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This repo is specifically intended for use with [Immich](https://immich.app/), a self-hosted photo library.
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---
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# Model Description
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This repo contains ONNX exports for the associated CLIP model by OpenCLIP. See the [OpenCLIP](https://github.com/mlfoundations/open_clip) repo for more info.
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This repo is specifically intended for use with [Immich](https://immich.app/), a self-hosted photo library.
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