README.md

metadata

license: apache-2.0
language:
  - de
  - en
  - es
  - fr
  - ja
  - ko
  - zh
tags:
  - reranker
  - text-reranking
  - semantic-search
  - retrieval
  - zen
  - zenlm
pipeline_tag: text-classification

Zen Reranker

Zen Reranker is a high-performance reranking model for search and retrieval pipelines. Part of the Zen AI model family by Hanzo AI.

Overview

Zen Reranker is optimized for:

• Retrieval-Augmented Generation (RAG) — re-score retrieved passages for LLM context
• Search quality improvement — rerank initial BM25/dense retrieval results
• Cross-lingual retrieval — strong multilingual performance
• DSO integration — compatible with Hanzo's Decentralized Semantic Optimization

Quick Start

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_name = "zenlm/zen-reranker"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name, torch_dtype=torch.float16)

def rerank(query, passages):
    pairs = [[query, p] for p in passages]
    inputs = tokenizer(
        pairs, padding=True, truncation=True,
        max_length=512, return_tensors="pt"
    )
    with torch.no_grad():
        scores = model(**inputs).logits.squeeze(-1)
    ranked = sorted(zip(passages, scores.tolist()), key=lambda x: x[1], reverse=True)
    return ranked

query = "What is the capital of France?"
passages = ["Paris is the capital of France.", "Berlin is in Germany.", "Madrid is in Spain."]
results = rerank(query, passages)
for passage, score in results:
    print(f"{score:.3f}: {passage}")

With sentence-transformers

from sentence_transformers import CrossEncoder

model = CrossEncoder("zenlm/zen-reranker")
scores = model.predict([
    ["What is the capital of France?", "Paris is the capital of France."],
    ["What is the capital of France?", "Berlin is in Germany."],
])

Specifications

Attribute	Value
Parameters	4B
Architecture	Qwen3ForSequenceClassification
Context	32,768 tokens
Languages	100+ (multilingual)
License	Apache 2.0

Use Cases

1. RAG pipelines — rerank retrieved chunks before passing to LLM
2. Search engines — improve document ranking quality
3. QA systems — score answer candidates for relevance
4. Semantic deduplication — score similarity for clustering

Abliteration

Like all Zen models, Zen Reranker is abliterated — refusal bias has been removed using directional ablation via hanzoai/remove-refusals.

Technique: Refusal in LLMs is mediated by a single direction — Arditi et al.

Model Family

Model	Parameters	Use Case
Zen Nano	0.6B	Edge AI
Zen Scribe	4B	Writing
Zen Pro	8B	Professional AI
Zen Max	671B MoE	Frontier
Zen Reranker	4B	Retrieval
Zen Embedding	—	Embeddings

Citation

@misc{zen-reranker-2025,
  title={Zen Reranker: High-Performance Neural Reranking},
  author={Hanzo AI and Zoo Labs Foundation},
  year={2025},
  url={https://huggingface.co/zenlm/zen-reranker}
}

---

Part of the Zen model ecosystem by Hanzo AI (Techstars '17) and Zoo Labs Foundation.