Instructions to use Felix-Zhenghao/paligemma2-3b-pt-448-img-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Felix-Zhenghao/paligemma2-3b-pt-448-img-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="Felix-Zhenghao/paligemma2-3b-pt-448-img-encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Felix-Zhenghao/paligemma2-3b-pt-448-img-encoder") model = AutoModel.from_pretrained("Felix-Zhenghao/paligemma2-3b-pt-448-img-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 525 Bytes
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"architectures": [
"SiglipVisionModel"
],
"attention_dropout": 0.0,
"hidden_act": "gelu_pytorch_tanh",
"hidden_size": 1152,
"image_size": 448,
"intermediate_size": 4304,
"layer_norm_eps": 1e-06,
"model_type": "siglip_vision_model",
"num_attention_heads": 16,
"num_channels": 3,
"num_hidden_layers": 27,
"num_image_tokens": 1024,
"num_positions": 256,
"patch_size": 14,
"projection_dim": 2304,
"torch_dtype": "bfloat16",
"transformers_version": "4.47.1",
"vision_use_head": false
}
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