Instructions to use Bingsu/cold_light_pass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bingsu/cold_light_pass with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Bingsu/cold_light_pass") 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("Bingsu/cold_light_pass") model = AutoModelForZeroShotImageClassification.from_pretrained("Bingsu/cold_light_pass") - Notebooks
- Google Colab
- Kaggle
Upload config
Browse files- config.json +2 -2
config.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
-
"_commit_hash": "
|
| 3 |
-
"_name_or_path": "
|
| 4 |
"architectures": [
|
| 5 |
"CLIPModel"
|
| 6 |
],
|
|
|
|
| 1 |
{
|
| 2 |
+
"_commit_hash": "94ecb85d628d5d571d003f4937ee0130ffec30cc",
|
| 3 |
+
"_name_or_path": "Bingsu/cold_light_pass",
|
| 4 |
"architectures": [
|
| 5 |
"CLIPModel"
|
| 6 |
],
|