Instructions to use SE6446/VitMix-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SE6446/VitMix-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SE6446/VitMix-v1", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("SE6446/VitMix-v1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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library_name: transformers
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# Model Card for Model ID
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This Model is mean't to serve more as a blueprint than a base. It has been trained of fashionmnist to prove that I can do tensor maths. It achieves an average loss of 0.4-ish.
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The code is in files. Do what you want!
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library_name: transformers
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license: wtfpl
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datasets:
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- fashion_mnist
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# Model Card for Model ID
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This Model is mean't to serve more as a blueprint than a base. It has been trained of fashionmnist to prove that I can do tensor maths. It achieves an average loss of 0.4-ish.
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The code is in files. Do what you want!
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