Instructions to use mentesniker/WizardCoder-1B-V1.0-ruby-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mentesniker/WizardCoder-1B-V1.0-ruby-summarization with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("WizardLM/WizardCoder-1B-V1.0") model = PeftModel.from_pretrained(base_model, "mentesniker/WizardCoder-1B-V1.0-ruby-summarization") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a755d90ae35bcb8a05febec4a4f92e6b4bb49211a768eb7829d183ffbdbf2d98
- Size of remote file:
- 13.4 MB
- SHA256:
- 6eb62f20179df1afbca6adc55bb301c12dd38c74e9630723d7d2fae0ecf75995
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.