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
Update config.json
Browse files- config.json +1 -0
config.json
CHANGED
|
@@ -88,6 +88,7 @@
|
|
| 88 |
"text_config_dict": {
|
| 89 |
"hidden_size": 768,
|
| 90 |
"intermediate_size": 3072,
|
|
|
|
| 91 |
"num_attention_heads": 12,
|
| 92 |
"num_hidden_layers": 12,
|
| 93 |
"projection_dim": 768
|
|
|
|
| 88 |
"text_config_dict": {
|
| 89 |
"hidden_size": 768,
|
| 90 |
"intermediate_size": 3072,
|
| 91 |
+
"max_position_embeddings": 514,
|
| 92 |
"num_attention_heads": 12,
|
| 93 |
"num_hidden_layers": 12,
|
| 94 |
"projection_dim": 768
|