Instructions to use Mohsen21/The_700_data_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mohsen21/The_700_data_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="Mohsen21/The_700_data_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("Mohsen21/The_700_data_model") model = AutoModelForTextToSpectrogram.from_pretrained("Mohsen21/The_700_data_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spm_char.model from Mohsen21/The_700_data_model: direct link, hf CLI and curl.
- Browser
- Download file 238 kB
-
https://huggingface.co/Mohsen21/The_700_data_model/resolve/main/spm_char.model
- Command line
-
hf download hf://Mohsen21/The_700_data_model/spm_char.model
-
curl -L -o spm_char.model https://huggingface.co/Mohsen21/The_700_data_model/resolve/main/spm_char.model
238 kB
- Xet hash:
- 4dc82f60ce6b29d9a35208f0d2bdecdcf961d82cfe1d43b92d72506ab8d06829
- Size of remote file:
- 238 kB
- SHA256:
- 7fcc48f3e225f627b1641db410ceb0c8649bd2b0c982e150b03f8be3728ab560
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.