Instructions to use fancyfeast/so400m-long with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fancyfeast/so400m-long with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="fancyfeast/so400m-long") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("fancyfeast/so400m-long") model = AutoModelForZeroShotImageClassification.from_pretrained("fancyfeast/so400m-long", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,120 Bytes
cf85c5f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | {
"architectures": [
"SiglipModel"
],
"initializer_factor": 1.0,
"model_type": "siglip",
"text_config": {
"_attn_implementation_autoset": true,
"_name_or_path": "../clip-training/checkpoints/jgi8443g/samples_9999872",
"architectures": [
"SiglipTextModel"
],
"attention_dropout": 0.0,
"hidden_act": "gelu_pytorch_tanh",
"hidden_size": 1152,
"intermediate_size": 4304,
"layer_norm_eps": 1e-06,
"max_position_embeddings": 256,
"model_type": "siglip_text_model",
"num_attention_heads": 16,
"num_hidden_layers": 27,
"projection_size": 1152,
"torch_dtype": "float32",
"vocab_size": 256000
},
"torch_dtype": "float32",
"transformers_version": "4.51.0.dev0",
"vision_config": {
"attention_dropout": 0.0,
"hidden_act": "gelu_pytorch_tanh",
"hidden_size": 1152,
"image_size": 384,
"intermediate_size": 4304,
"layer_norm_eps": 1e-06,
"model_type": "siglip_vision_model",
"num_attention_heads": 16,
"num_channels": 3,
"num_hidden_layers": 27,
"patch_size": 14,
"torch_dtype": "float32"
}
}
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