Feature Extraction
Transformers
Safetensors
f2p_decoder
vision
image-reconstruction
siglip2
custom_code
Instructions to use toilaluan/f2p_decoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toilaluan/f2p_decoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="toilaluan/f2p_decoder", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("toilaluan/f2p_decoder", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 942 Bytes
09b2c2d | 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 | {
"architectures": [
"F2PDecoderModel"
],
"attention_probs_dropout_prob": 0.0,
"auto_map": {
"AutoConfig": "configuration_f2p_decoder.F2PDecoderConfig",
"AutoModel": "modeling_f2p_decoder.F2PDecoderModel"
},
"decoder_hidden_size": 1152,
"decoder_intermediate_size": 4096,
"decoder_num_attention_heads": 16,
"decoder_num_hidden_layers": 28,
"drop_cls_token": true,
"dtype": "float32",
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1152,
"image_mean": [
0.5,
0.5,
0.5
],
"image_size": 224,
"image_std": [
0.5,
0.5,
0.5
],
"initializer_range": 0.02,
"layer_norm_eps": 1e-12,
"model_type": "f2p_decoder",
"num_channels": 3,
"num_patches": 256,
"patch_size": 14,
"pretrained_encoder_name": "google/siglip2-so400m-patch14-224",
"qkv_bias": true,
"source_decoder_repo": "nyu-visionx/siglip2_decoder",
"transformers_version": "4.57.6"
}
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