Instructions to use rendchevi/text-to-code-v0.1-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rendchevi/text-to-code-v0.1-lora with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rendchevi/text-to-code-v0.1-lora", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9b436ac5476d208bb01200a7b76dbffabe12c466095c2e2fe74852896f8e3455
- Size of remote file:
- 15.4 MB
- SHA256:
- d0a75270c4c1aab635aa24596f87bb5eb324c9f3e368c579b1b66508e7d696c5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.