Instructions to use nvidia/C-RADIOv4-H with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/C-RADIOv4-H with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nvidia/C-RADIOv4-H", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/C-RADIOv4-H", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -47,11 +47,11 @@ Hugging Face: 01/23/2026 via [RADIO Collection of Models](https://huggingface.co
|
|
| 47 |
|
| 48 |
## References
|
| 49 |
|
| 50 |
-
* [
|
| 51 |
-
* [
|
| 52 |
-
* [
|
| 53 |
-
* [
|
| 54 |
-
* [
|
| 55 |
|
| 56 |
## Model Architecture
|
| 57 |
|
|
|
|
| 47 |
|
| 48 |
## References
|
| 49 |
|
| 50 |
+
* [AM-RADIO: Agglomerative Vision Foundation Model -- Reduce All Domains Into One](https://arxiv.org/abs/2312.06709)
|
| 51 |
+
* [PHI-S: Distribution Balancing for Label-Free Multi-Teacher Distillation](https://arxiv.org/abs/2410.01680)
|
| 52 |
+
* [RADIOv2.5: Improved Baselines for Agglomerative Vision Foundation Models](https://arxiv.org/abs/2412.07679)
|
| 53 |
+
* [FeatSharp: Your Vision Model Features, Sharper](https://arxiv.org/abs/2502.16025)
|
| 54 |
+
* [C-RADIOv4 (Tech Report)](https://arxiv.org/abs/2601.17237)
|
| 55 |
|
| 56 |
## Model Architecture
|
| 57 |
|