Instructions to use diffraction-zebra/hse_seminar_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use diffraction-zebra/hse_seminar_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="diffraction-zebra/hse_seminar_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("diffraction-zebra/hse_seminar_1") model = AutoModel.from_pretrained("diffraction-zebra/hse_seminar_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from diffraction-zebra/hse_seminar_1: direct link, hf CLI and curl.
- Browser
- Download file 1 MB
-
https://huggingface.co/diffraction-zebra/hse_seminar_1/resolve/main/spiece.model
- Command line
-
hf download hf://diffraction-zebra/hse_seminar_1/spiece.model
-
curl -L -o spiece.model https://huggingface.co/diffraction-zebra/hse_seminar_1/resolve/main/spiece.model
1 MB
- Xet hash:
- 66b8222ec2b95cb833d677338c3a7b46f96b5884022527419c5364517ac050c0
- Size of remote file:
- 1 MB
- SHA256:
- c43d5d89523c7b40603a3164ce65244b8f604adea7c191ab7ce55f3ef3907700
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