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_datasetii3.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 1.02 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/b0cabf8afeef7eb6b72f215e699e99e655515084/tokenized_train_datasetii3.pt
- Command line
-
hf download hf://dilip025/dummy-model@b0cabf8afeef7eb6b72f215e699e99e655515084/tokenized_train_datasetii3.pt
-
curl -L -o tokenized_train_datasetii3.pt https://huggingface.co/dilip025/dummy-model/resolve/b0cabf8afeef7eb6b72f215e699e99e655515084/tokenized_train_datasetii3.pt
1.02 GB
- Xet hash:
- fb2cbfc9144c731d7663b99243d886e1563c5e3a05e78bab300c8a37135a64da
- Size of remote file:
- 1.02 GB
- SHA256:
- fa6f4cc0f5d5bf1f2ccebe7ffeed74b2c2e01c4fa784d97f1267cc021eda64ee
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.