Instructions to use nvidia/OpenMath-CodeLlama-70b-Python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use nvidia/OpenMath-CodeLlama-70b-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/0.0.7 from nvidia/OpenMath-CodeLlama-70b-Python: direct link, hf CLI and curl.
- Browser
- Download file 16.8 MB
-
https://huggingface.co/nvidia/OpenMath-CodeLlama-70b-Python/resolve/main/nemo_model/model_weights/model.decoder.layers.self_attention.linear_proj.weight/0.0.7
- Command line
-
hf download hf://nvidia/OpenMath-CodeLlama-70b-Python/nemo_model/model_weights/model.decoder.layers.self_attention.linear_proj.weight/0.0.7
-
curl -L -o 0.0.7 https://huggingface.co/nvidia/OpenMath-CodeLlama-70b-Python/resolve/main/nemo_model/model_weights/model.decoder.layers.self_attention.linear_proj.weight/0.0.7
16.8 MB
- Xet hash:
- 5ae191510d7f204e81a223579c26030cc847ea73700788d0b9fc742033e9d626
- Size of remote file:
- 16.8 MB
- SHA256:
- d6ffc70c88f01c5030340655af4bd902b41ee0f218879219c53cbefb11a596b0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.