Instructions to use hf-internal-testing/tiny-random-UdopModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-UdopModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-UdopModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-UdopModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-UdopModel") - Notebooks
- Google Colab
- Kaggle
File size: 903 Bytes
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"architectures": [
"UdopModel"
],
"bos_token_id": 0,
"d_ff": 37,
"d_kv": 8,
"d_model": 32,
"decoder_start_token_id": 0,
"dense_act_fn": "relu",
"dropout_rate": 0.1,
"eos_token_id": 1,
"feed_forward_proj": "relu",
"image_size": 224,
"initializer_factor": 0.002,
"is_encoder_decoder": true,
"is_gated_act": false,
"layer_norm_epsilon": 1e-06,
"max_2d_position_embeddings": 1024,
"model_type": "udop",
"num_channels": 3,
"num_decoder_layers": 5,
"num_heads": 4,
"num_layers": 5,
"pad_token_id": 0,
"patch_size": 16,
"relative_attention_max_distance": 128,
"relative_attention_num_buckets": 32,
"relative_bias_args": [
{
"type": "1d"
},
{
"type": "horizontal"
},
{
"type": "vertical"
}
],
"torch_dtype": "float32",
"transformers_version": "4.40.0.dev0",
"use_cache": true,
"vocab_size": 33201
}
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