Instructions to use isemmanuelolowe/BerKANT_171M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use isemmanuelolowe/BerKANT_171M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="isemmanuelolowe/BerKANT_171M", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("isemmanuelolowe/BerKANT_171M", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("isemmanuelolowe/BerKANT_171M", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
BerKANT (training)
A Bert implementation where most of the torch.nn.linear have been replaced with KANLinear.
Currently pretraining on JackBAI/bert_pretrain_datasets on a RTX 4090. Will be do in 5 days from 13/05/2024. Until then :)
from transformers import AutoModelForMaskedLM, AutoConfig
# Define the model path
model_path = "isemmanuelolowe/BerKANT_171M"
# Load the configuration
config = AutoConfig.from_pretrained(model_path)
# Load the model with the correct configuration
model = AutoModelForMaskedLM.from_pretrained(model_path, config=config, trust_remote_code=True)
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