Instructions to use Slakhwani/MarketMail with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Slakhwani/MarketMail with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-1b7") model = PeftModel.from_pretrained(base_model, "Slakhwani/MarketMail") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d2315a52ffee00c137575b5b0e3f9dc2f3f1ad17adcb1a22f6e1871f696f56f
- Size of remote file:
- 12.6 MB
- SHA256:
- 5cb890f3159b85a72a05de54aa1912e672427059b33cb1c52447147acbbfa962
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.