Instructions to use h4rr9/mnist_1bit_text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use h4rr9/mnist_1bit_text with Transformers:
# Load model directly from transformers import AutoTokenizer, Tim tokenizer = AutoTokenizer.from_pretrained("h4rr9/mnist_1bit_text") model = Tim.from_pretrained("h4rr9/mnist_1bit_text", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from h4rr9/mnist_1bit_text: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/h4rr9/mnist_1bit_text/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://h4rr9/mnist_1bit_text/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/h4rr9/mnist_1bit_text/resolve/main/pytorch_model.bin
504 MB
- Xet hash:
- 68c16cea36f54a153a7ffe8c68480616590622fc968c828adbe2de8956a4cad0
- Size of remote file:
- 504 MB
- SHA256:
- 0658bcbe22ceb496123456cb04c887a5d8a200fe806396b643c2db7939ef04c5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.