Instructions to use hf-tiny-model-private/tiny-random-LayoutLMModel 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-LayoutLMModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-LayoutLMModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-tiny-model-private/tiny-random-LayoutLMModel: direct link, hf CLI and curl.
- Browser
- Download file 890 kB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-LayoutLMModel/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-LayoutLMModel@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-tiny-model-private/tiny-random-LayoutLMModel/resolve/refs%2Fpr%2F1/model.safetensors
890 kB
- Xet hash:
- 7806350dd2dd29c2360ed031c648e0267b5df1edd53814ddfafa8bff212a70f7
- Size of remote file:
- 890 kB
- SHA256:
- 07b5aac9d80582a191cc9a31a2c5a9c9095ded8e2bd35b40c19360fecaa9b2a1
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