Instructions to use disanda/first_try_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use disanda/first_try_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="disanda/first_try_4")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("disanda/first_try_4") model = AutoModelForMaskedLM.from_pretrained("disanda/first_try_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from disanda/first_try_4: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/disanda/first_try_4/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://disanda/first_try_4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/disanda/first_try_4/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- dd332899ed04360ade68bdf31245d9aa7ae5f505d28f760c794097108cbf9a0f
- Size of remote file:
- 268 MB
- SHA256:
- ecebafe5f5f323b184af4de01a4dd7844e1205bc20bbbf151c08e81b70ebae8b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.