Instructions to use Migga/ViT_Chess_V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Migga/ViT_Chess_V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Migga/ViT_Chess_V1") 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("Migga/ViT_Chess_V1") model = AutoModelForImageClassification.from_pretrained("Migga/ViT_Chess_V1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Migga/ViT_Chess_V1: direct link, hf CLI and curl.
- Browser
- Download file 1.13 GB
-
https://huggingface.co/Migga/ViT_Chess_V1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Migga/ViT_Chess_V1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Migga/ViT_Chess_V1/resolve/main/pytorch_model.bin
1.13 GB
- Xet hash:
- c326d58ad68b5662998fe8fa4a4a1045c647fea79c07a4701a516a4cf6d77996
- Size of remote file:
- 1.13 GB
- SHA256:
- 3f081e676bd1023c3bd7424f0fb600c0fa1f3c1326899e09113a876753318a77
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