Instructions to use nvidia/MambaVision-B-1K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/MambaVision-B-1K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nvidia/MambaVision-B-1K", 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("nvidia/MambaVision-B-1K", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download mambavision_base_1k.pth.tar from nvidia/MambaVision-B-1K: direct link, hf CLI and curl.
- Browser
- Download file 1.17 GB
-
https://huggingface.co/nvidia/MambaVision-B-1K/resolve/main/mambavision_base_1k.pth.tar
- Command line
-
hf download hf://nvidia/MambaVision-B-1K/mambavision_base_1k.pth.tar
-
curl -L -o mambavision_base_1k.pth.tar https://huggingface.co/nvidia/MambaVision-B-1K/resolve/main/mambavision_base_1k.pth.tar
1.17 GB
- Xet hash:
- 83f7980be6c483dc7b9d008c05ad1a5ddb282ffa96e29526bcac6b48c5a44339
- Size of remote file:
- 1.17 GB
- SHA256:
- 8d41ac2875fe9da18dd7d5064099cbfe9ec5d49e358056e443040bc8030e91d5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.