Instructions to use ashm/test-docs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ashm/test-docs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="ashm/test-docs") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("ashm/test-docs") model = AutoModelForZeroShotImageClassification.from_pretrained("ashm/test-docs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 762 Bytes
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"architectures": [
"CLIPModel"
],
"model_type": "clip",
"model_cfg": {
"embed_dim": 512,
"vision_cfg": {
"timm_model_name": "vit_base_patch16_224",
"timm_model_pretrained": false,
"timm_pool": "",
"timm_proj": "linear",
"image_size": 224
},
"text_cfg": {
"hf_model_name": "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract",
"hf_tokenizer_name": "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract",
"proj": "mlp",
"pooler_type": "cls_last_hidden_state_pooler",
"context_length": 256
}
},
"preprocess_cfg": {
"mean": [
0.48145466,
0.4578275,
0.40821073
],
"std": [
0.26862954,
0.26130258,
0.27577711
]
}
} |