Instructions to use nvidia/OpenMath-CodeLlama-13b-Python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use nvidia/OpenMath-CodeLlama-13b-Python with NeMo:
# tag did not correspond to a valid NeMo domain.
- Notebooks
- Google Colab
- Kaggle
Download nemo_model/model_weights/model.decoder.layers.mlp.linear_fc1.layer_norm_weight/.zarray from nvidia/OpenMath-CodeLlama-13b-Python: direct link, hf CLI and curl.
- Browser
- Download file 230 Bytes
-
https://huggingface.co/nvidia/OpenMath-CodeLlama-13b-Python/resolve/e9dae23dfb337ed78099ad69951e2636c09f2651/nemo_model/model_weights/model.decoder.layers.mlp.linear_fc1.layer_norm_weight/.zarray
- Command line
-
hf download hf://nvidia/OpenMath-CodeLlama-13b-Python@e9dae23dfb337ed78099ad69951e2636c09f2651/nemo_model/model_weights/model.decoder.layers.mlp.linear_fc1.layer_norm_weight/.zarray
-
curl -L -o .zarray https://huggingface.co/nvidia/OpenMath-CodeLlama-13b-Python/resolve/e9dae23dfb337ed78099ad69951e2636c09f2651/nemo_model/model_weights/model.decoder.layers.mlp.linear_fc1.layer_norm_weight/.zarray
230 Bytes
- Xet hash:
- b178c25f5dae1e6315d48ea7e482ccb1f843be07a541c8005ec3ab4bfebbf947
- Size of remote file:
- 230 Bytes
- SHA256:
- b3916ec779470f49c2d4157b668b7ad1708b67fc2abd42bdad82a8f7e1ba84e7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.