Fill-Mask
Transformers
Safetensors
English
modernbert_small
babylm
babylm-2026
modernbert
masked-language-model
strict-small
custom_code
Instructions to use remg1997/modernbert-small-modernbert-small-factorized-linear-babylm2026 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use remg1997/modernbert-small-modernbert-small-factorized-linear-babylm2026 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="remg1997/modernbert-small-modernbert-small-factorized-linear-babylm2026", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("remg1997/modernbert-small-modernbert-small-factorized-linear-babylm2026", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb0f67435af96cd18bee6fb1e26ac192a2e0af2072c28fe3fa2ddffade5098aa
- Size of remote file:
- 1.47 kB
- SHA256:
- c59d98e7f49d748953e6dfad099811cb493aa2b1d98d54450091e6a3176535ca
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.