Instructions to use hf-internal-testing/tiny-random-vision_perceiver_conv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-vision_perceiver_conv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-vision_perceiver_conv") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoTokenizer, AutoModelForImageClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-vision_perceiver_conv") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-vision_perceiver_conv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0d16e6ef29fc18f6833722fddc7ef66d120efa2f56ac0e000017ed136f74974d
- Size of remote file:
- 618 kB
- SHA256:
- f5a73477df4916ca7b8281dce7d6fe24107dc079e99e809e388bd147696360f3
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