Instructions to use hypha-space/lenet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hypha-space/lenet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hypha-space/lenet", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("hypha-space/lenet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from hypha-space/lenet: direct link, hf CLI and curl.
- Browser
- Download file 314 Bytes
-
https://huggingface.co/hypha-space/lenet/resolve/main/config.json
- Command line
-
hf download hf://hypha-space/lenet/config.json
-
curl -L -o config.json https://huggingface.co/hypha-space/lenet/resolve/main/config.json
314 Bytes
| { | |
| "architectures": [ | |
| "LeNetModelForImageClassification" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_lenet.LeNetConfig", | |
| "AutoModelForImageClassification": "modeling_lenet.LeNetModelForImageClassification" | |
| }, | |
| "dtype": "float32", | |
| "model_type": "lenet", | |
| "transformers_version": "4.57.1" | |
| } | |