Instructions to use mahan13900/Mahan-Nano-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mahan13900/Mahan-Nano-Base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mahan13900/Mahan-Nano-Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Mahan Nano Base
This repository is for the Mahan Nano base model. The current artifact is a tokenizer; no trained model weights are supplied.
Tokenizer files and the training report are in tokenizer/.
from transformers import AutoTokenizer
t = AutoTokenizer.from_pretrained('mahan13900/Mahan-Nano-Base', subfolder='tokenizer', token=True)
ids = t('سلام میروم', add_special_tokens=False, split_special_tokens=True)['input_ids']
ids.append(t.eos_token_id)
Special tokens alone do not create reasoning or fill-in-the-middle ability. Evaluate downstream behavior after training the model.
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support