Any-to-Any
Transformers
Safetensors
English
NemotronH_Nano_Omni_Reasoning_V3
feature-extraction
nvidia
nemotron
multimodal
quantized
4-bit precision
int4
custom_code
compressed-tensors
Instructions to use drawais/Nemotron-3-Nano-Omni-30B-A3B-W4A16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use drawais/Nemotron-3-Nano-Omni-30B-A3B-W4A16 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("drawais/Nemotron-3-Nano-Omni-30B-A3B-W4A16", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bac61eb61252c8dd3c1d9784323a20942e5ddf47eda3c8f55aefd39f4aa7edf5
- Size of remote file:
- 1.41 GB
- SHA256:
- 7219d0d4509d5569d9c96cdd987aeb26f1e259b0eae5f87c71a29eab1040c667
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.