Instructions to use immanuelpeter/C-RADIOv4-H with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use immanuelpeter/C-RADIOv4-H with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="immanuelpeter/C-RADIOv4-H")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("immanuelpeter/C-RADIOv4-H", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from immanuelpeter/C-RADIOv4-H: direct link, hf CLI and curl.
- Browser
- Download file 2.88 kB
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https://huggingface.co/immanuelpeter/C-RADIOv4-H/resolve/main/README.md
- Command line
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hf download hf://immanuelpeter/C-RADIOv4-H/README.md
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curl -L -o README.md https://huggingface.co/immanuelpeter/C-RADIOv4-H/resolve/main/README.md
2.88 kB
| license: other | |
| license_name: nvidia-open-model-agreement | |
| license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/ | |
| pipeline_tag: image-feature-extraction | |
| library_name: transformers | |
| tags: | |
| - vision | |
| base_model: | |
| - nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 | |
| # C-RADIOv4-H | |
| This repository packages the C-RADIOv4-H copy inside | |
| [Nemotron 3 Nano Omni](https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16). | |
| It is not a clone of NVIDIA's standalone encoder | |
| [nvidia/C-RADIOv4-H](https://huggingface.co/nvidia/C-RADIOv4-H). | |
| ## Contents | |
| | File | Tensors | What it holds | | |
| |---|---|---| | |
| | `model.safetensors` | 390 | RADIO Tower, extracted from Omni `vision_model.*` | | |
| | `projector.safetensors` | 3 | Omni `mlp1` Projector | | |
| | `config.json` | | Vision-only `RADIOModel` config, with source and revision | | |
| | `projector_config.json`, `projector.py` | | Projector shapes and loader | | |
| ## Architecture | |
| | Component | Details | | |
| |---|---| | |
| | Tower | C-RADIOv4-H, 1280 hidden, patch size 16 | | |
| | Token compression | InternVL v2 2x2 pixel shuffle, scale 0.5, no learned parameters | | |
| | Projector (`mlp1`) | `RMSNorm(5120)`, `Linear(5120, 20480)` no bias, SquaredReLU, `Linear(20480, 2688)` no bias | | |
| | Extra | `video_embedder` Linear on the RADIO patch generator (2-frame tubelets) | | |
| The standalone encoder does not ship this Omni Projector path. Omni SFT later stages | |
| train more than the Projector, so the ViT tensors can differ from | |
| [nvidia/C-RADIOv4-H](https://huggingface.co/nvidia/C-RADIOv4-H). | |
| ## Usage | |
| See [`examples/inference.py`](examples/inference.py) for image feature extraction. | |
| `AutoModel.from_pretrained` on this repository will fail. The example builds RADIO | |
| from the Omni `vision_config` (`trust_remote_code=True`) and loads the weights here. | |
| ## Validation | |
| The [`parity script`](https://github.com/immanuel-peter/vision-tower-bench/blob/main/tests/test_parity.py) | |
| compares all 390 Tower tensors and three Projector tensors with the pinned Omni | |
| parent using `torch.equal`. That is bit-identity with Omni `vision_model.*` and | |
| `mlp1.*`, not with [nvidia/C-RADIOv4-H](https://huggingface.co/nvidia/C-RADIOv4-H). | |
| ## Reproduction | |
| The [export script](https://github.com/immanuel-peter/vision-tower-bench/blob/main/scripts/export_nemotron_omni_vision.py) | |
| reads `vision_model.*` (390 tensors) and `mlp1.*` (3 tensors) from shard 1 of | |
| `nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16`. It removes the prefixes and | |
| writes the original BF16 tensors. | |
| ## Credits | |
| NVIDIA released [C-RADIOv4-H](https://huggingface.co/nvidia/C-RADIOv4-H) and | |
| [Nemotron 3 Nano Omni](https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16). | |
| The Omni paper is [arXiv:2604.24954](https://arxiv.org/abs/2604.24954). | |
| ## License | |
| [NVIDIA Open Model Agreement](LICENSE), the same license as the source model. | |