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/11.0 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/11.0
- Command line
-
hf download hf://nvidia/OpenMath-CodeLlama-7b-Python/nemo_model/model_weights/model.decoder.layers.mlp.linear_fc1.layer_norm_weight/11.0
-
curl -L -o 11.0 https://huggingface.co/nvidia/OpenMath-CodeLlama-7b-Python/resolve/main/nemo_model/model_weights/model.decoder.layers.mlp.linear_fc1.layer_norm_weight/11.0
8.19 kB
- Xet hash:
- c7f16be800b4f99905b570b1954aa6293b0114113183772a19a6e94fb04f942e
- Size of remote file:
- 8.19 kB
- SHA256:
- 5d761e0ecb06d12cf1e7dbc050f934fba16a099c3aa7bda3c8068429f0552ffc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.