Instructions to use tulayaka/loadtest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tulayaka/loadtest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tulayaka/loadtest")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tulayaka/loadtest") model = AutoModelForSequenceClassification.from_pretrained("tulayaka/loadtest", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tulayaka/loadtest: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/tulayaka/loadtest/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tulayaka/loadtest/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tulayaka/loadtest/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 03c6f25410fdf47cfe95ce949786d88d4fc3089d182d61b20bed8e0db3565542
- Size of remote file:
- 438 MB
- SHA256:
- 184f65b6586d5a2e7156f33c03fb6096d2dec91da772b31a069b433129818e68
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