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.mlp.linear_fc1.weight/1.9.0 from nvidia/OpenMath-CodeLlama-34b-Python: direct link, hf CLI and curl.
- Browser
- Download file 45.1 MB
-
https://huggingface.co/nvidia/OpenMath-CodeLlama-34b-Python/resolve/main/nemo_model/model_weights/model.decoder.layers.mlp.linear_fc1.weight/1.9.0
- Command line
-
hf download hf://nvidia/OpenMath-CodeLlama-34b-Python/nemo_model/model_weights/model.decoder.layers.mlp.linear_fc1.weight/1.9.0
-
curl -L -o 1.9.0 https://huggingface.co/nvidia/OpenMath-CodeLlama-34b-Python/resolve/main/nemo_model/model_weights/model.decoder.layers.mlp.linear_fc1.weight/1.9.0
45.1 MB
- Xet hash:
- e8618fb4258c5164c97e26d5416a3e714a2223abe5ab78e8dd2947c992332f74
- Size of remote file:
- 45.1 MB
- SHA256:
- 0744c4f64c87c7392a85b7f2e3b2049b01cc93bbc8d3bb27c3781d1a2cf69140
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.