Instructions to use nvidia/OpenMath-CodeLlama-34b-Python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use nvidia/OpenMath-CodeLlama-34b-Python with NeMo:
# tag did not correspond to a valid NeMo domain.
- Notebooks
- Google Colab
- Kaggle
Download nemo_model/model_weights/model.decoder.layers.self_attention.linear_proj.weight/10.0.6 from nvidia/OpenMath-CodeLlama-34b-Python: direct link, hf CLI and curl.
- Browser
- Download file 16.8 MB
-
https://huggingface.co/nvidia/OpenMath-CodeLlama-34b-Python/resolve/main/nemo_model/model_weights/model.decoder.layers.self_attention.linear_proj.weight/10.0.6
- Command line
-
hf download hf://nvidia/OpenMath-CodeLlama-34b-Python/nemo_model/model_weights/model.decoder.layers.self_attention.linear_proj.weight/10.0.6
-
curl -L -o 10.0.6 https://huggingface.co/nvidia/OpenMath-CodeLlama-34b-Python/resolve/main/nemo_model/model_weights/model.decoder.layers.self_attention.linear_proj.weight/10.0.6
16.8 MB
- Xet hash:
- 738b8d3eb8e5e55553967512ae76b497f015e913c7577e7a5d634a7cf25f0500
- Size of remote file:
- 16.8 MB
- SHA256:
- bc4877360ee9f99458f7266db7f55a39d43d77f8b2bb4ed1bb195cc00ee04efb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.