Instructions to use Mathnub/rubert-tiny-sberquad-6ep-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mathnub/rubert-tiny-sberquad-6ep-1 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="Mathnub/rubert-tiny-sberquad-6ep-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Mathnub/rubert-tiny-sberquad-6ep-1") model = AutoModelForQuestionAnswering.from_pretrained("Mathnub/rubert-tiny-sberquad-6ep-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Mathnub/rubert-tiny-sberquad-6ep-1: direct link, hf CLI and curl.
- Browser
- Download file 46.8 MB
-
https://huggingface.co/Mathnub/rubert-tiny-sberquad-6ep-1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Mathnub/rubert-tiny-sberquad-6ep-1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Mathnub/rubert-tiny-sberquad-6ep-1/resolve/main/pytorch_model.bin
46.8 MB
- Xet hash:
- f669fe50cbf3152ab5b8991385409aa7e81ac826808f7714920aa7d1a134e843
- Size of remote file:
- 46.8 MB
- SHA256:
- a3766124e894f6567177aa7b6f04223ffc526faa7108e5ebc81a8ea3b9dcade3
路
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