Instructions to use hf-internal-testing/tiny-random-CLIPModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-CLIPModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="hf-internal-testing/tiny-random-CLIPModel") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-CLIPModel") model = AutoModelForZeroShotImageClassification.from_pretrained("hf-internal-testing/tiny-random-CLIPModel") - Notebooks
- Google Colab
- Kaggle
Fix projection dim
#3
by Xenova HF Staff - opened
- config.json +1 -1
config.json
CHANGED
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@@ -65,7 +65,7 @@
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| 65 |
"pad_token_id": 1,
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| 66 |
"prefix": null,
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| 67 |
"problem_type": null,
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| 68 |
-
"projection_dim":
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| 69 |
"pruned_heads": {},
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| 70 |
"remove_invalid_values": false,
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| 71 |
"repetition_penalty": 1.0,
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| 65 |
"pad_token_id": 1,
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| 66 |
"prefix": null,
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| 67 |
"problem_type": null,
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| 68 |
+
"projection_dim": 64,
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| 69 |
"pruned_heads": {},
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| 70 |
"remove_invalid_values": false,
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| 71 |
"repetition_penalty": 1.0,
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