Model Details
Full Model IDnvidia/llama-nemotron-rerank-1b-v2
Pipeline / Tasktext-ranking
Librarytransformers
Downloads (all-time)536.4K
Likes50
Last Modified4/10/2026
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-1b-v2")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
transformerspytorchsafetensorsllama_bidirectext-classificationtextrerankercross-encoderretrievalsemantic-searchtext-rankingcustom_codemultilinguallicense:othertext-embeddings-inferenceendpoints_compatibleregion: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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⬇ Downloads536.4K
❤️ Community Likes50
🛠️ 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.