Model Details
Full Model IDnvidia/llama-nemotron-rerank-vl-1b-v2
Pipeline / Tasktext-ranking
Librarytransformers
Downloads (all-time)134.1K
Likes33
Last Modified4/9/2026
Author / Orgnvidia
PrivateNo — public
⚡ Quick Usage (Python)
Using the 🤗 Transformers library. Install with pip install transformers
from transformers import pipeline
# Load the model
pipe = pipeline("text-ranking", model="nvidia/llama-nemotron-rerank-vl-1b-v2")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
transformerssafetensorsllama_nemotron_vl_rerankfeature-extractionrerankercross-encodervisual-document-retrievalquestion-answering retrievalmultimodal rerankingsemantic-searchragsentence-transformerstext-rankingcustom_codemultilingualarxiv:2501.14818license:otherregion:us
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Access model files, inference API, and full documentation on Hugging Face.
Open on Hugging Face →Browse Model Files ↗← Browse All Models🤖 Task: text-ranking
This model is designed for the text-ranking task. Explore more models for this use case.
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⬇ Downloads134.1K
❤️ Community Likes33
🛠️ Requirements
- →Install: pip install transformers
- →Python 3.8+ recommended for Transformers.
- →GPU (CUDA) speeds up inference significantly.
- →Use model.half() for fp16 on limited VRAM.