Instructions to use zenlm/zen-reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zenlm/zen-reranker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zenlm/zen-reranker")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-reranker") model = AutoModelForCausalLM.from_pretrained("zenlm/zen-reranker", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Zen Reranker
4B-parameter cross-encoder reranker for retrieval pipelines and search relevance — re-scores candidate passages for RAG and search quality. Part of the Zen reranker line.
Repackaged from Qwen/Qwen3-Reranker-4B (apache-2.0, Alibaba Qwen). Native HuggingFace safetensors, re-hosted under the Zen reranker line. Not trained from scratch — a permissively-licensed redistribution for the OSS-clean Zen model line.
Usage
Score query/passage pairs following the upstream Qwen/Qwen3-Reranker-4B recipe (yes/no token scoring).
Hosted via the Hanzo gateway (api.hanzo.ai) as zen-reranker. GGUF build for CPU inference: zenlm/zen-reranker-4B-GGUF.
License
apache-2.0. Upstream: Qwen/Qwen3-Reranker-4B by Alibaba Qwen. This repository redistributes the weights under the same license.
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