Feature Extraction
Transformers
Safetensors
qwen3_vl
worl
nvrm
reward-model
video-reward-model
qwen3-vl
Instructions to use Efficient-Large-Model/WORL-NVRM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Efficient-Large-Model/WORL-NVRM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Efficient-Large-Model/WORL-NVRM")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("Efficient-Large-Model/WORL-NVRM") model = AutoModel.from_pretrained("Efficient-Large-Model/WORL-NVRM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download worl-bundle.json from Efficient-Large-Model/WORL-NVRM: direct link, hf CLI and curl.
- Browser
- Download file 5.32 kB
-
https://huggingface.co/Efficient-Large-Model/WORL-NVRM/resolve/main/worl-bundle.json
- Command line
-
hf download hf://Efficient-Large-Model/WORL-NVRM/worl-bundle.json
-
curl -L -o worl-bundle.json https://huggingface.co/Efficient-Large-Model/WORL-NVRM/resolve/main/worl-bundle.json
5.32 kB
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| "metadata": { | |
| "architecture": "qwen3_vl_scalar_reward", | |
| "backbone_class": "Qwen3VLModel", | |
| "backbone_state_format": "qwen3_vl_model_unprefixed_safetensors", | |
| "base_model_id": "Qwen/Qwen3-VL-4B-Instruct", | |
| "base_model_revision": "ebb281ec70b05090aa6165b016eac8ec08e71b17", | |
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