Instructions to use dilip025/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dilip025/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dilip025/dummy-model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dilip025/dummy-model") model = AutoModelForMaskedLM.from_pretrained("dilip025/dummy-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenized_train_datasetii_long.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/925b4322d65f6972a59aa0216e382dc1ab3c8fb8/tokenized_train_datasetii_long.pt
- Command line
-
hf download hf://dilip025/dummy-model@925b4322d65f6972a59aa0216e382dc1ab3c8fb8/tokenized_train_datasetii_long.pt
-
curl -L -o tokenized_train_datasetii_long.pt https://huggingface.co/dilip025/dummy-model/resolve/925b4322d65f6972a59aa0216e382dc1ab3c8fb8/tokenized_train_datasetii_long.pt
1.42 GB
- Xet hash:
- 9e34ac1283dfb50a6d7e4e8d20d99de27026f06a7340e36865e579c8ff7301e0
- Size of remote file:
- 1.42 GB
- SHA256:
- 840315f1df19a08d4765d3872031bc4e899d1710486f54f52b130917e9949c1e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.