Instructions to use hf-internal-testing/tiny-random-GLPNForDepthEstimation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-GLPNForDepthEstimation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="hf-internal-testing/tiny-random-GLPNForDepthEstimation")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-GLPNForDepthEstimation") model = AutoModelForDepthEstimation.from_pretrained("hf-internal-testing/tiny-random-GLPNForDepthEstimation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-GLPNForDepthEstimation: direct link, hf CLI and curl.
- Browser
- Download file 3.11 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-GLPNForDepthEstimation/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-GLPNForDepthEstimation@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-GLPNForDepthEstimation/resolve/refs%2Fpr%2F1/model.safetensors
3.11 MB
- Xet hash:
- a8c36aa8ff1a8f39d110b3aa1678af52088a3c858547de1ae67fb5424c3fd9b3
- Size of remote file:
- 3.11 MB
- SHA256:
- d5d3308d9e895f5472b7cc74ffeb9a20e21445f828eada80b505c4d60fc420ae
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.