Instructions to use rajlab/condensatenet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rajlab/condensatenet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="rajlab/condensatenet", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rajlab/condensatenet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from rajlab/condensatenet: direct link, hf CLI and curl.
- Browser
- Download file 502 Bytes
-
https://huggingface.co/rajlab/condensatenet/resolve/main/config.json
- Command line
-
hf download hf://rajlab/condensatenet/config.json
-
curl -L -o config.json https://huggingface.co/rajlab/condensatenet/resolve/main/config.json
502 Bytes
| { | |
| "architectures": [ | |
| "CondensateNet" | |
| ], | |
| "dropout_rate": 0.15, | |
| "dtype": "float32", | |
| "encoder_variant": "rw_s", | |
| "model_type": "condensatenet", | |
| "num_classes": 1, | |
| "pyramid_channels": [ | |
| 24, | |
| 48, | |
| 64, | |
| 160 | |
| ], | |
| "pyramid_dim": 32, | |
| "spatial_kernel_size": 11, | |
| "transformers_version": "4.57.3", | |
| "use_spatial_attention": true, | |
| "auto_map": { | |
| "AutoConfig": "configuration_condensatenet.CondensateNetConfig", | |
| "AutoModel": "modeling_condensatenet.CondensateNet" | |
| } | |
| } |