Instructions to use nvidia/OpenMath-CodeLlama-7b-Python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use nvidia/OpenMath-CodeLlama-7b-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/23.0 from nvidia/OpenMath-CodeLlama-7b-Python: direct link, hf CLI and curl.
- Browser
- Download file 8.19 kB
-
https://huggingface.co/nvidia/OpenMath-CodeLlama-7b-Python/resolve/main/nemo_model/model_weights/model.decoder.layers.mlp.linear_fc1.layer_norm_weight/23.0
- Command line
-
hf download hf://nvidia/OpenMath-CodeLlama-7b-Python/nemo_model/model_weights/model.decoder.layers.mlp.linear_fc1.layer_norm_weight/23.0
-
curl -L -o 23.0 https://huggingface.co/nvidia/OpenMath-CodeLlama-7b-Python/resolve/main/nemo_model/model_weights/model.decoder.layers.mlp.linear_fc1.layer_norm_weight/23.0
8.19 kB
- Xet hash:
- 5084b50141a461832964f503b68fcbf49ae63393b3a8a38b088917838e1594d6
- Size of remote file:
- 8.19 kB
- SHA256:
- 5b24a71c3608515a64fa5d6666554ded1b37cb5b56295ea2d3042d30846d4b53
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.