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.final_layernorm.weight/.zarray from nvidia/OpenMath-CodeLlama-34b-Python: direct link, hf CLI and curl.
- Browser
- Download file 207 Bytes
-
https://huggingface.co/nvidia/OpenMath-CodeLlama-34b-Python/resolve/main/nemo_model/model_weights/model.decoder.final_layernorm.weight/.zarray
- Command line
-
hf download hf://nvidia/OpenMath-CodeLlama-34b-Python/nemo_model/model_weights/model.decoder.final_layernorm.weight/.zarray
-
curl -L -o .zarray https://huggingface.co/nvidia/OpenMath-CodeLlama-34b-Python/resolve/main/nemo_model/model_weights/model.decoder.final_layernorm.weight/.zarray
207 Bytes
- Xet hash:
- 96d1fcffa2d18e162ca869fc2fd9450c791ca64847180351a789234b7024a209
- Size of remote file:
- 207 Bytes
- SHA256:
- a79a4a7e13f7d36fdbd516372f73511e69cb78cc03b3ab3b898e9d02f2013587
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.