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 NOTICE.txt from Efficient-Large-Model/WORL-NVRM: direct link, hf CLI and curl.
- Browser
- Download file 297 Bytes
-
https://huggingface.co/Efficient-Large-Model/WORL-NVRM/resolve/main/NOTICE.txt
- Command line
-
hf download hf://Efficient-Large-Model/WORL-NVRM/NOTICE.txt
-
curl -L -o NOTICE.txt https://huggingface.co/Efficient-Large-Model/WORL-NVRM/resolve/main/NOTICE.txt
297 Bytes
| NVRM-World | |
| Copyright (c) 2026 NVIDIA Corporation and affiliates. All rights reserved. | |
| Licensed under the Apache License, Version 2.0. | |
| See LICENSE.txt for the full license text. | |
| This release includes a merged Qwen/Qwen3-VL-4B-Instruct backbone, distributed under the Apache License, Version 2.0. | |