Any-to-Any
Transformers
Safetensors
PyTorch
NemotronH_Nano_Omni_Reasoning_V3
feature-extraction
nvidia
multimodal
custom_code
Instructions to use Jashan887/76_Nvidia_Reasoning_30B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jashan887/76_Nvidia_Reasoning_30B with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Jashan887/76_Nvidia_Reasoning_30B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d49b1662d97a03ea1f90c44312961472a325493b62240fc8473b4e035407873
- Size of remote file:
- 17.1 MB
- SHA256:
- e5e7dc84d72e8f248321611c3d6dce23407b135f55f8caf5b26119798d12f85f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.