Fill-Mask
Transformers
PyTorch
English
babylm
babylm-2026
strict-small
gpt-bert
muon
adamuon
sample-efficient-pretraining
custom_code
Instructions to use svsatheesh/BabySteps_MurphysLaw-10M-mixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use svsatheesh/BabySteps_MurphysLaw-10M-mixed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="svsatheesh/BabySteps_MurphysLaw-10M-mixed", trust_remote_code=True)# Load model directly from transformers import GPTBERTFoCausalLM model = GPTBERTFoCausalLM.from_pretrained("svsatheesh/BabySteps_MurphysLaw-10M-mixed", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2c4fa3085fdd465043f8e20d0d937d060978f040fd1b4c83c3a1197daf96b8dd
- Size of remote file:
- 132 MB
- SHA256:
- 0338d54a6628980c6217d2bc39cc58d0c9bb8c6fda776ce765be75706b832812
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