Instructions to use hf-tiny-model-private/tiny-random-XmodForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-XmodForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-XmodForSequenceClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XmodForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-XmodForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-tiny-model-private/tiny-random-XmodForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-XmodForSequenceClassification/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-XmodForSequenceClassification@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-tiny-model-private/tiny-random-XmodForSequenceClassification/resolve/refs%2Fpr%2F1/model.safetensors
32.2 MB
- Xet hash:
- 5071b78ad00d5c895d0717aefdfd3f496806571d8e35a0cfd08c03e612f5445a
- Size of remote file:
- 32.2 MB
- SHA256:
- 72a5cfb9b130a81d58bad8f20c1c65e2e88432d33ae8809126917d8f77ce6112
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