Instructions to use nesanchezo/model_prueba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nesanchezo/model_prueba with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nesanchezo/model_prueba") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("nesanchezo/model_prueba") model = AutoModelForImageClassification.from_pretrained("nesanchezo/model_prueba", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 987 Bytes
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"_name_or_path": "farleyknight-org-username/vit-base-mnist",
"architectures": [
"ViTForImageClassification"
],
"attention_probs_dropout_prob": 0.0,
"encoder_stride": 16,
"finetuning_task": "image-classification",
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 768,
"id2label": {
"0": "0",
"1": "1",
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},
"image_size": 224,
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"0": "0",
"1": "1",
"2": "2",
"3": "3",
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},
"layer_norm_eps": 1e-12,
"model_type": "vit",
"num_attention_heads": 12,
"num_channels": 3,
"num_hidden_layers": 12,
"patch_size": 16,
"problem_type": "single_label_classification",
"qkv_bias": true,
"torch_dtype": "float32",
"transformers_version": "4.30.2"
}
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