Instructions to use MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_dP52EnQd 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_3-Depth_2-Node_dP52EnQd 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_3-Depth_2-Node_dP52EnQd") 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_3-Depth_2-Node_dP52EnQd") model = AutoModelForImageClassification.from_pretrained("MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_dP52EnQd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload ViTForImageClassification
Browse files- config.json +2 -2
config.json
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
{
|
| 2 |
-
"_name_or_path": "./dataset/./model_heritage_dataset/LoRA_finetuning_rank16/vit/pet37__component_3__depth_2__uuid_dP52EnQd",
|
| 3 |
"architectures": [
|
| 4 |
"ViTForImageClassification"
|
| 5 |
],
|
|
@@ -99,5 +99,5 @@
|
|
| 99 |
"qkv_bias": true,
|
| 100 |
"return_dict": false,
|
| 101 |
"torch_dtype": "float32",
|
| 102 |
-
"transformers_version": "4.40.
|
| 103 |
}
|
|
|
|
| 1 |
{
|
| 2 |
+
"_name_or_path": "/cs/labs/yedid/eliahu.horwitz/research_projects/ModelHeritage/ModelHeritage/dataset/./model_heritage_dataset/LoRA_finetuning_rank16/vit/pet37__component_3__depth_2__uuid_dP52EnQd",
|
| 3 |
"architectures": [
|
| 4 |
"ViTForImageClassification"
|
| 5 |
],
|
|
|
|
| 99 |
"qkv_bias": true,
|
| 100 |
"return_dict": false,
|
| 101 |
"torch_dtype": "float32",
|
| 102 |
+
"transformers_version": "4.40.1"
|
| 103 |
}
|