Instructions to use hf-internal-testing/tiny-random-BitModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BitModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-BitModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-BitModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-BitModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-BitModel: direct link, hf CLI and curl.
- Browser
- Download file 89.4 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-BitModel/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-BitModel@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-BitModel/resolve/refs%2Fpr%2F1/model.safetensors
89.4 kB
- Xet hash:
- b78a176d74bc6d0525e46875e8a85b3ec604fcaa045d3dd7c95610b5f56d717f
- Size of remote file:
- 89.4 kB
- SHA256:
- eb3b79f8b8f2e32d7104fdefae9187f2dca09aaa79e55f6282fa355accdddb09
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