Model Details
Full Model IDjhu-clsp/rank1-7b
Pipeline / Tasktext-ranking
Librarytransformers
Downloads (all-time)466.6K
Likes4
Last Modified4/8/2025
Author / Orgjhu-clsp
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="jhu-clsp/rank1-7b")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
transformerssafetensorsqwen2text-generationrerankerretrievaltext-rankingendataset:jhu-clsp/rank1-training-dataarxiv:2502.18418base_model:Qwen/Qwen2.5-7Bbase_model:finetune:Qwen/Qwen2.5-7Blicense:mittext-embeddings-inferenceendpoints_compatibleregion:us
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This model is designed for the text-ranking task. Explore more models for this use case.
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⬇ Downloads466.6K
❤️ Community Likes4
🛠️ 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.