Feature Extraction
Transformers
PyTorch
TensorFlow
ONNX
Safetensors
roberta
text-embeddings-inference
Instructions to use hf-internal-testing/tiny-random-RobertaModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-RobertaModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-RobertaModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-RobertaModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-RobertaModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-RobertaModel: direct link, hf CLI and curl.
- Browser
- Download file 352 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-RobertaModel/resolve/main/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-RobertaModel/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-RobertaModel/resolve/main/model.safetensors
352 kB
- Xet hash:
- 9bcdd3a5fa488a040544d117679424fb54a94986e0f1162dd6ac8766850b2749
- Size of remote file:
- 352 kB
- SHA256:
- 174f949b916d6139bc04699819bed6d22a8c7343924805a3a350cbcb364fb88d
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