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antflydb/mxbai-rerank-base-v1
mxbai-rerank-base-v1 is a text ranking model from antflydb. Use it for the text ranking task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
This repository packages Mixedbread's mxbai-rerank-base-v1 reranker for Antfly Inference deployments. It includes the original safetensors checkpoint plus Antfly Inference GGUF variants for local CPU/GPU inference.
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.gguf398 MB · 51%
From the Hugging Face model README
This repository packages Mixedbread's mxbai-rerank-base-v1 reranker for Antfly Inference deployments. It includes the original safetensors checkpoint plus Antfly Inference GGUF variants for local CPU/GPU inference.
The model is a cross-encoder reranker: given a query and a set of candidate documents, it scores each query/document pair so retrieval systems can re-order candidates after lexical or embedding search.
| File | Purpose |
|---|---|
model.safetensors | Original fp32/fp16-compatible Transformers checkpoint |
mxbai-rerank-base-v1.Q8_0.gguf | Higher-fidelity GGUF quantization |
mxbai-rerank-base-v1.Q4_K.gguf | Smaller GGUF quantization for lower memory use |
config.json | Model architecture/configuration |
tokenizer.json, spm.model, tokenizer sidecars | Tokenization assets |
model_manifest.json | Antfly Inference model capability manifest |
antfly_inference_variants.json | Antfly Inference GGUF variant index |
antfly inference pull antflydb/mxbai-rerank-base-v1:gguf:Q8_0
antfly inference run
Use :gguf:Q4_K instead for the smaller rank-oriented artifact.
curl -X POST http://localhost:8082/rerank \
-H "Content-Type: application/json" \
-d '{
"model": "antflydb/mxbai-rerank-base-v1",
"query": "Who wrote To Kill a Mockingbird?",
"documents": [
"To Kill a Mockingbird is a novel by Harper Lee.",
"Moby-Dick was written by Herman Melville.",
"Jane Austen wrote Pride and Prejudice."
]
}'
The GGUF files were exported with Antfly's inference exporter from the mixedbread-ai/mxbai-rerank-base-v1 safetensors source.
| Variant | File | Size | Notes |
|---|---|---|---|
| Q8_0 | mxbai-rerank-base-v1.Q8_0.gguf | ~278 MB | Better score fidelity; DeBERTa word embeddings remain dense F16 for CUDA compatibility |
| Q4_K | mxbai-rerank-base-v1.Q4_K.gguf | ~103 MB | Smaller footprint; rank-validated, with larger absolute score drift than Q8_0 |
Validated locally with Antfly CUDA rerank on:
Results versus the safetensors CUDA baseline:
| Variant | Top-1 Ordering | Max Absolute Score Drift |
|---|---|---|
| Q8_0 | Preserved on all validation cases | 0.0029 |
| Q4_K | Preserved on all validation cases | 0.1261 |
Q4_K should be treated as a ranking-oriented compact artifact. Downstream systems that threshold absolute reranker scores should prefer model.safetensors or Q8_0.
This package is based on mixedbread-ai/mxbai-rerank-base-v1.
If you use this model, cite the upstream Mixedbread reranker work:
@online{rerank2024mxbai,
title={Boost Your Search With The Crispy Mixedbread Rerank Models},
author={Aamir Shakir and Darius Koenig and Julius Lipp and Sean Lee},
year={2024},
url={https://www.mixedbread.ai/blog/mxbai-rerank-v1},
}
Apache 2.0. See LICENSE.