Instructions to use julien-c/dummy-diff-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use julien-c/dummy-diff-tokenizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="julien-c/dummy-diff-tokenizer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("julien-c/dummy-diff-tokenizer") model = AutoModelForMaskedLM.from_pretrained("julien-c/dummy-diff-tokenizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from julien-c/dummy-diff-tokenizer: direct link, hf CLI and curl.
- Browser
- Download file 65.1 kB
-
https://huggingface.co/julien-c/dummy-diff-tokenizer/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://julien-c/dummy-diff-tokenizer@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/julien-c/dummy-diff-tokenizer/resolve/refs%2Fpr%2F2/pytorch_model.bin
65.1 kB
- Xet hash:
- 0ef7dba7b1bda16ab1fcc8d67504190aa8d581854303d332ba3d0f1b42aefc5a
- Size of remote file:
- 65.1 kB
- SHA256:
- 4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.