Any-to-Any
Transformers
Safetensors
PyTorch
NemotronH_Nano_Omni_Reasoning_V3
feature-extraction
nvidia
multimodal
custom_code
Eval Results
Instructions to use nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add ParseBench evaluation results
#16
by boyang-runllama - opened
This PR ensures your model shows up at https://huggingface.co/datasets/llamaindex/ParseBench.
This is based on the new evaluation results feature: https://huggingface.co/docs/hub/eval-results.
Note: this includes per-dimension performance across all 5 ParseBench dimensions (text_content, text_formatting, layout, chart, table) along with the overall mean score.