Instructions to use mohammadmahdinouri/final_expressive_student_init with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohammadmahdinouri/final_expressive_student_init with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, FlowModelForDistillation tokenizer = AutoTokenizer.from_pretrained("mohammadmahdinouri/final_expressive_student_init") model = FlowModelForDistillation.from_pretrained("mohammadmahdinouri/final_expressive_student_init", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from mohammadmahdinouri/final_expressive_student_init: direct link, hf CLI and curl.
- Browser
- Download file 514 kB
-
https://huggingface.co/mohammadmahdinouri/final_expressive_student_init/resolve/main/tokenizer.model
- Command line
-
hf download hf://mohammadmahdinouri/final_expressive_student_init/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/mohammadmahdinouri/final_expressive_student_init/resolve/main/tokenizer.model
514 kB
- Xet hash:
- f15891305d8a91d17a223fa5f04a85531d1d51fd30af72ce98ffc9eb5966cee1
- Size of remote file:
- 514 kB
- SHA256:
- 1608b90876103c6c3c67ca079ab9d3c5ee4e7707acf869103e728a4d30626643
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.