Instructions to use Basitmalik/trainc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Basitmalik/trainc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Basitmalik/trainc") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Basitmalik/trainc") model = AutoModelForImageClassification.from_pretrained("Basitmalik/trainc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Basitmalik/trainc: direct link, hf CLI and curl.
- Browser
- Download file 111 MB
-
https://huggingface.co/Basitmalik/trainc/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Basitmalik/trainc@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Basitmalik/trainc/resolve/refs%2Fpr%2F1/pytorch_model.bin
111 MB
- Xet hash:
- d6392023567cba77afdeb7a6d0eee2cc48b36b750654475d89e1fcc92bc6d994
- Size of remote file:
- 111 MB
- SHA256:
- 198c443d9a07aeb9dcdda8fa026a4dbd52d300053fd090addf6023c495a76e8d
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