Fill-Mask
Transformers
PyTorch
Safetensors
gpt_bert
feature-extraction
gpt-bert
babylm
remote-code
custom_code
Instructions to use jumelet/gptbert-tur-100steps-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jumelet/gptbert-tur-100steps-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jumelet/gptbert-tur-100steps-small", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jumelet/gptbert-tur-100steps-small", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tur-2gpu-100steps.bin from jumelet/gptbert-tur-100steps-small: direct link, hf CLI and curl.
- Browser
- Download file 145 MB
-
https://huggingface.co/jumelet/gptbert-tur-100steps-small/resolve/main/tur-2gpu-100steps.bin
- Command line
-
hf download hf://jumelet/gptbert-tur-100steps-small/tur-2gpu-100steps.bin
-
curl -L -o tur-2gpu-100steps.bin https://huggingface.co/jumelet/gptbert-tur-100steps-small/resolve/main/tur-2gpu-100steps.bin
145 MB
- Xet hash:
- 8c5b7291487223b728e145bd0e14d6b7b124608d712fe73942202c304bf36e2e
- Size of remote file:
- 145 MB
- SHA256:
- b6953d5bcd05a54431d799c4cc4050cb507565e3b473ba32e2bf3be22693014e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.