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 tokenizer.json from Efficient-Large-Model/WORL-NVRM: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/Efficient-Large-Model/WORL-NVRM/resolve/main/tokenizer.json
- Command line
-
hf download hf://Efficient-Large-Model/WORL-NVRM/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Efficient-Large-Model/WORL-NVRM/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 693ec4b3922b0bd306bf7b4989e115ffbfeb7b0c08b31bc6d956818c6bb07f61
- Size of remote file:
- 11.4 MB
- SHA256:
- aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
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