Instructions to use miladfa7/picth_vision_checkpoint_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use miladfa7/picth_vision_checkpoint_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="miladfa7/picth_vision_checkpoint_3")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("miladfa7/picth_vision_checkpoint_3") model = AutoModelForVideoClassification.from_pretrained("miladfa7/picth_vision_checkpoint_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from miladfa7/picth_vision_checkpoint_3: direct link, hf CLI and curl.
- Browser
- Download file 345 MB
-
https://huggingface.co/miladfa7/picth_vision_checkpoint_3/resolve/main/model.safetensors
- Command line
-
hf download hf://miladfa7/picth_vision_checkpoint_3/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/miladfa7/picth_vision_checkpoint_3/resolve/main/model.safetensors
345 MB
- Xet hash:
- d87eacedace24014b121af3897513271ef992d85a4d52050551706d4547ec03b
- Size of remote file:
- 345 MB
- SHA256:
- 6c27609215795155ec1a6fdd7a5fd3c41a859a54104cc77b8d14be88b6d337cf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.