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:
- 6dd9e6d02e64c2c95b0ecfebaec8880467081dd50a38a3ad9c782ed5ff1ac27a
- Size of remote file:
- 29.9 MB
- SHA256:
- b8b63172d5ffcde5508be0a767119755d608666013911b225095ad32ab5b5c87
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