seamon67/Zerank-1-GGUF

About

This model was converted to GGUF format from zeroentropy/zerank-1-reranker using a modified version of llama.cpp (release b10269). Refer to the original model card for more details on the model.

This is a working GGUF.

Most community GGUFs of Zerank-1-Reranker produce garbage scores because of 4 issues:

1. Zerank 1 ships modeling_zeranker.py instead of 1_LogitScore directory.
2. Wrong Token Extracted -> yes (9693) vs the correct Yes (9454). Also, no false token on Zerank 1.
3. Wrong Scoring Formula -> Zerank 1 needs sigmoid(yes_logit / 5).
4. Slightly different chat template.

This GGUF fixes all 4 problems.

Original Model Card

Releasing zeroentropy/zerank-1

In search engines, rerankers are crucial for improving the accuracy of your retrieval system.

However, SOTA rerankers are closed-source and proprietary. At ZeroEntropy, we've trained a SOTA reranker outperforming closed-source competitors, and we're launching our model here on HuggingFace.

This reranker outperforms proprietary rerankers such as cohere-rerank-v3.5 and Salesforce/LlamaRank-v1 across a wide variety of domains, including finance, legal, code, STEM, medical, and conversational data.

At ZeroEntropy we've developed an innovative multi-stage pipeline that models query-document relevance scores as adjusted Elo ratings. See our Technical Report (Coming soon!) for more details.

This model is released under the Apache License 2.0.

For this model's smaller twin, see zerank-1-small, which we've fully open-sourced under an Apache 2.0 License.

Model Details

Property	Value
Parameters	4B
Context Length	32,768 tokens (32k)
Base Model	Qwen/Qwen3-4B
License	Apache-2.0

How to Use

from sentence_transformers import CrossEncoder

model = CrossEncoder("zeroentropy/zerank-1", trust_remote_code=True)

query_documents = [
    ("What is 2+2?", "4"),
    ("What is 2+2?", "The answer is definitely 1 million"),
]

scores = model.predict(query_documents)
print(scores)

The model can also be inferenced using ZeroEntropy's /models/rerank endpoint.

Evaluations

NDCG@10 scores between zerank-1 and competing closed-source proprietary rerankers. Since we are evaluating rerankers, OpenAI's text-embedding-3-small is used as an initial retriever for the Top 100 candidate documents.

Task	Embedding	cohere-rerank-v3.5	Salesforce/Llama-rank-v1	zerank-1-small	zerank-1
Code	0.678	0.724	0.694	0.730	0.754
Conversational	0.250	0.571	0.484	0.556	0.596
Finance	0.839	0.824	0.828	0.861	0.894
Legal	0.703	0.804	0.767	0.817	0.821
Medical	0.619	0.750	0.719	0.773	0.796
STEM	0.401	0.510	0.595	0.680	0.694

License

This model is licensed under the Apache License 2.0.