Image-Text-to-Text
MLX
English
nemotron
nemotron-h
mamba
mamba2
ssm
mixture-of-experts
multimodal
vision
audio
video
speech
omni
reasoning
jang
JANGTQ2
apple-silicon
Instructions to use JANGQ-AI/Nemotron-3-Nano-Omni-30B-A3B-JANGTQ2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use JANGQ-AI/Nemotron-3-Nano-Omni-30B-A3B-JANGTQ2 with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("JANGQ-AI/Nemotron-3-Nano-Omni-30B-A3B-JANGTQ2") config = load_config("JANGQ-AI/Nemotron-3-Nano-Omni-30B-A3B-JANGTQ2") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Upload README.md with huggingface_hub
Browse files
README.md
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| Variant | Size | Tok/s | Loader |
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| [MXFP4](https://huggingface.co/
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| [JANGTQ4](https://huggingface.co/JANGQ-AI/Nemotron-3-Nano-Omni-30B-A3B-JANGTQ4) | 19.9 GB | ~82 | `jang_tools.load_jangtq` |
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| [JANGTQ2](https://huggingface.co/JANGQ-AI/Nemotron-3-Nano-Omni-30B-A3B-JANGTQ2) | 12.6 GB | ~85 | `jang_tools.load_jangtq` |
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| [MXFP4 (Osaurus)](https://huggingface.co/OsaurusAI/Nemotron-3-Nano-Omni-30B-A3B-MXFP4) | 22.6 GB | ~113 | `mlx_lm.load` |
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| [JANGTQ4](https://huggingface.co/JANGQ-AI/Nemotron-3-Nano-Omni-30B-A3B-JANGTQ4) | 19.9 GB | ~82 | `jang_tools.load_jangtq` |
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| [JANGTQ2](https://huggingface.co/JANGQ-AI/Nemotron-3-Nano-Omni-30B-A3B-JANGTQ2) | 12.6 GB | ~85 | `jang_tools.load_jangtq` |
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