Instructions to use not-lain/hfcustomarch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use not-lain/hfcustomarch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="not-lain/hfcustomarch", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("not-lain/hfcustomarch", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "not-lain/hfcustomarch", | |
| "architectures": [ | |
| "MnistModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "not-lain/hfcustomarch--MyConfig.MnistConfig", | |
| "AutoModelForImageClassification": "not-lain/hfcustomarch--MyModel.MnistModel" | |
| }, | |
| "conv1": 10, | |
| "conv2": 20, | |
| "custom_pipelines": { | |
| "image-classification": { | |
| "impl": "MyPipe.MnistPipe", | |
| "pt": [ | |
| "AutoModelForImageClassification" | |
| ], | |
| "tf": [], | |
| "type": "image" | |
| } | |
| }, | |
| "model_type": "MobileNetV1", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.39.0.dev0" | |
| } | |