---
pipeline_tag: text-ranking
tags:
- gguf
- reranker
- qwen3
- llama-cpp
language:
- multilingual
base_model: jinaai/jina-reranker-v3
base_model_relation: quantized
inference: false
license: cc-by-nc-4.0
library_name: llama.cpp
---

# jina-reranker-v3-GGUF

GGUF quantizations of [jina-reranker-v3](https://huggingface.co/jinaai/jina-reranker-v3) using llama.cpp. A 0.6B parameter multilingual listwise reranker quantized for efficient inference.

## Requirements

- Python 3.8+
- llama.cpp binaries (`llama-embedding` and `llama-tokenize`)
- Hanxiao's llama.cpp fork recommended: https://github.com/hanxiao/llama.cpp

## Installation

```bash
pip install numpy safetensors
```

## Files

- `jina-reranker-v3-BF16.gguf` - Quantized model weights (BF16, 1.1GB)
- `projector.safetensors` - MLP projector weights (3MB)
- `rerank.py` - Reranker implementation

## Usage

```python
from rerank import GGUFReranker

# Initialize reranker
reranker = GGUFReranker(
    model_path="jina-reranker-v3-BF16.gguf",
    projector_path="projector.safetensors",
    llama_embedding_path="/path/to/llama-embedding"
)

# Rerank documents
query = "What is the capital of France?"
documents = [
    "Paris is the capital and largest city of France.",
    "Berlin is the capital of Germany.",
    "The Eiffel Tower is located in Paris."
]

results = reranker.rerank(query, documents)

for result in results:
    print(f"Score: {result['relevance_score']:.4f}, Doc: {result['document'][:50]}...")
```

## API

### `GGUFReranker.rerank(query, documents, top_n=None, return_embeddings=False, instruction=None)`

**Arguments:**
- `query` (str): Search query
- `documents` (List[str]): Documents to rerank
- `top_n` (int, optional): Return only top N results
- `return_embeddings` (bool): Include embeddings in output
- `instruction` (str, optional): Custom ranking instruction

**Returns:**
List of dicts with keys: `index`, `relevance_score`, `document`, and optionally `embedding`



## Citation

If you find `jina-reranker-v3` useful in your research, please cite the [original paper](https://arxiv.org/abs/2509.25085):

```bibtex
@misc{wang2025jinarerankerv3lateinteractiondocument,
      title={jina-reranker-v3: Last but Not Late Interaction for Document Reranking},
      author={Feng Wang and Yuqing Li and Han Xiao},
      year={2025},
      eprint={2509.25085},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.25085},
}
```

## License

This MLX implementation follows the same CC BY-NC 4.0 license as the original model. For commercial usage inquiries, please [contact Jina AI](https://jina.ai/contact-sales/).