Instructions to use hf-internal-testing/tiny-random-UMT5Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-UMT5Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-UMT5Model")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-UMT5Model") model = AutoModelForMultimodalLM.from_pretrained("hf-internal-testing/tiny-random-UMT5Model") - Notebooks
- Google Colab
- Kaggle
File size: 690 Bytes
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"architectures": [
"UMT5Model"
],
"bos_token_id": 2,
"classifier_dropout": 0.0,
"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",
"initializer_factor": 0.002,
"is_encoder_decoder": true,
"is_gated_act": false,
"layer_norm_epsilon": 1e-06,
"model_type": "t5",
"num_decoder_layers": 5,
"num_heads": 4,
"num_layers": 5,
"pad_token_id": 0,
"relative_attention_max_distance": 128,
"relative_attention_num_buckets": 8,
"torch_dtype": "float32",
"transformers_version": "4.32.0.dev0",
"use_cache": true,
"vocab_size": 256300
}
|