Instructions to use MoTHer-VTHR/VTHR-LoRA-F-ModelTree_4-Depth_2-Node_tGeUP4bP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MoTHer-VTHR/VTHR-LoRA-F-ModelTree_4-Depth_2-Node_tGeUP4bP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MoTHer-VTHR/VTHR-LoRA-F-ModelTree_4-Depth_2-Node_tGeUP4bP") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("MoTHer-VTHR/VTHR-LoRA-F-ModelTree_4-Depth_2-Node_tGeUP4bP") model = AutoModelForImageClassification.from_pretrained("MoTHer-VTHR/VTHR-LoRA-F-ModelTree_4-Depth_2-Node_tGeUP4bP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload ViTForImageClassification
Browse files- config.json +2 -2
config.json
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{
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"_name_or_path": "./dataset/./model_heritage_dataset/LoRA_finetuning_rank16/vit/clevr-distance__component_4__depth_2__uuid_tGeUP4bP",
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"architectures": [
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"ViTForImageClassification"
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],
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"qkv_bias": true,
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"return_dict": false,
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"torch_dtype": "float32",
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"transformers_version": "4.40.
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}
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{
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"_name_or_path": "/cs/labs/yedid/eliahu.horwitz/research_projects/ModelHeritage/ModelHeritage/dataset/./model_heritage_dataset/LoRA_finetuning_rank16/vit/clevr-distance__component_4__depth_2__uuid_tGeUP4bP",
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"architectures": [
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"ViTForImageClassification"
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],
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"qkv_bias": true,
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"return_dict": false,
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"torch_dtype": "float32",
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"transformers_version": "4.40.1"
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}
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