Instructions to use hf-internal-testing/tiny-random-MimiModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MimiModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-MimiModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoFeatureExtractor, AutoModel extractor = AutoFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-MimiModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-MimiModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-MimiModel: direct link, hf CLI and curl.
- Browser
- Download file 1.9 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-MimiModel/resolve/main/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-MimiModel/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-MimiModel/resolve/main/model.safetensors
1.9 MB
- Xet hash:
- e04c321ebc27af6455d52115f7c0fa16418617830eb96b013db4ec093d45dff2
- Size of remote file:
- 1.9 MB
- SHA256:
- eeb4cb9ecfc6ea485aecaffb005c447d7b2545e03841b53fb5dfbec8858e2197
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.