Instructions to use WesScivetti/random_100M_short_1_10_20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WesScivetti/random_100M_short_1_10_20 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="WesScivetti/random_100M_short_1_10_20", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("WesScivetti/random_100M_short_1_10_20", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
WesScivetti/random_100M_short_1_10_20
Custom GPT-BERT checkpoint from the NPN filtered-corpus training experiments.
This repository includes custom architecture code for Transformers 5.x.
Install transformers>=5,<6 and torch>=2.6 before loading the .bin checkpoint. Example:
from transformers import AutoModelForMaskedLM, AutoTokenizer
repo_id = "WesScivetti/random_100M_short_1_10_20"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForMaskedLM.from_pretrained(repo_id, trust_remote_code=True)
model.eval()
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