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:
- f415af5fd9a4a4c8a53c1bf1d11ac023d83aba20e33cfd10ea2241db4cf929ee
- Size of remote file:
- 5.27 kB
- SHA256:
- 980eaa2905705a1697a48bdb7773aa5e29deb6c36e690e9091045b1c9f910622
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