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
| { | |
| "architectures": [ | |
| "GPTBERTFoCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_gpt_bert.ModelConfig", | |
| "AutoModel": "modeling_gpt_bert.GPTBERT", | |
| "AutoModelForCausalLM": "modeling_gpt_bert.GPTBERTForCausalLM", | |
| "AutoModelForMaskedLM": "modeling_gpt_bert.GPTBERTForMaskedLM" | |
| }, | |
| "attention_probs_dropout_prob": 0.1, | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 384, | |
| "intermediate_size": 1280, | |
| "max_position_embeddings": 512, | |
| "position_bucket_size": 32, | |
| "num_attention_heads": 6, | |
| "num_hidden_layers": 12, | |
| "vocab_size": 16384, | |
| "layer_norm_eps": 1.0e-7 | |
| } |