Instructions to use hf-internal-testing/tiny-random-UnivNetModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-UnivNetModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-UnivNetModel")# Load model directly from transformers import AutoFeatureExtractor, AutoModel extractor = AutoFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-UnivNetModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-UnivNetModel") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "UnivNetModel" | |
| ], | |
| "initializer_range": 0.01, | |
| "kernel_predictor_conv_size": 3, | |
| "kernel_predictor_dropout": 0.0, | |
| "kernel_predictor_hidden_channels": 8, | |
| "kernel_predictor_num_blocks": 3, | |
| "leaky_relu_slope": 0.2, | |
| "model_hidden_channels": 8, | |
| "model_in_channels": 8, | |
| "model_type": "univnet", | |
| "num_mel_bins": 20, | |
| "resblock_dilation_sizes": [ | |
| [ | |
| 1, | |
| 3, | |
| 9, | |
| 27 | |
| ], | |
| [ | |
| 1, | |
| 3, | |
| 9, | |
| 27 | |
| ], | |
| [ | |
| 1, | |
| 3, | |
| 9, | |
| 27 | |
| ] | |
| ], | |
| "resblock_kernel_sizes": [ | |
| 3, | |
| 3, | |
| 3 | |
| ], | |
| "resblock_stride_sizes": [ | |
| 8, | |
| 8, | |
| 4 | |
| ], | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.37.0.dev0" | |
| } | |