Instructions to use ConvLLaVA/ConvLLaVA-ConvNeXt-1024 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ConvLLaVA/ConvLLaVA-ConvNeXt-1024 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ConvLLaVA/ConvLLaVA-ConvNeXt-1024", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ConvLLaVA/ConvLLaVA-ConvNeXt-1024: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/ConvLLaVA/ConvLLaVA-ConvNeXt-1024/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ConvLLaVA/ConvLLaVA-ConvNeXt-1024/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ConvLLaVA/ConvLLaVA-ConvNeXt-1024/resolve/main/pytorch_model.bin
1.34 GB
- Xet hash:
- 5efae1c6653130ba486662aa102a32af089f6a764d4d10f0d033869fd3405a0e
- Size of remote file:
- 1.34 GB
- SHA256:
- a6867f0daa4b8aec2f86acb2a0fb1ea9ffc62acdc179fbabc225d68bcaf7b131
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.