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
Remove vLLM NVFP4 tp>1 caveat
Browse files
README.md
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@@ -248,8 +248,6 @@ Efficient Video Sampling: video-pruning-rate=0.5 drops 50% of redundant video to
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#### Platform-Specific Notes
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> **RTX Pro:** Due to a current bug with FlashInfer + RTX Pro, append: `--moe-backend triton`
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> **NVFP4 + TP>1:** Due to a current bug with the TRTLLM_GEN MoE backend kernels on vLLM, when running with TP>1 on NVFP4, append: `--moe-backend flashinfer_cutlass`
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##### vLLM on DGX Spark (aarch64 / ARM64)
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#### Platform-Specific Notes
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> **RTX Pro:** Due to a current bug with FlashInfer + RTX Pro, append: `--moe-backend triton`
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##### vLLM on DGX Spark (aarch64 / ARM64)
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