Instructions to use namdp-ptit/ViDense with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use namdp-ptit/ViDense with Transformers:
# Load model directly from transformers import AutoTokenizer, ViDense tokenizer = AutoTokenizer.from_pretrained("namdp-ptit/ViDense") model = ViDense.from_pretrained("namdp-ptit/ViDense", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sparse_linear.pt from namdp-ptit/ViDense: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/namdp-ptit/ViDense/resolve/main/sparse_linear.pt
- Command line
-
hf download hf://namdp-ptit/ViDense/sparse_linear.pt
-
curl -L -o sparse_linear.pt https://huggingface.co/namdp-ptit/ViDense/resolve/main/sparse_linear.pt
3.52 kB
- Xet hash:
- ac5dd8f02f4c89b8a86ba9d22dd4345b3b61b7e46825b9ef6c3b33e902772381
- Size of remote file:
- 3.52 kB
- SHA256:
- 45c93804d2142b8f6d7ec6914ae23a1eee9c6a1d27d83d908a20d2afb3595ad9
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