alt.freeweights.embed / mxbai-rerank-xsmall-v1
From: mixedbread-ai <huggingface.co/mixedbread-ai>Newsgroups: alt.freeweights.embedSubject:

mxbai-rerank-xsmall-v1

Date: 2026-11-14Commit: b5c6e9dSigned: 2DC28CC7B26A283B ✓

Text ranking model with 71M parameters. Onnx and safetensors weights, 524 MB in all. Published by mixedbread-ai under Apache-2.0.

71M params524MBmxbai-rerank-xsmall-v1onnx safetensorspermissive
FactsParams71MSize524 MBFormatsonnx, safetensorsTasktext rankingLicenseApache-2.0From sourcepermissive
$ fw get mixedbread-ai/mxbai-rerank-xsmall-v1
# manifest sha256 c0a034b70074… · 524 MB · 13 files
$ fw verify ./mxbai-rerank-xsmall-v1 mixedbread-ai/mxbai-rerank-xsmall-v1
From sourceHosted (signed)mixedbread-ai/mxbai-rerank-xsmall-v1Last check: 2026-11-14 21:10 UTC

Get help: custom fine-tune or private deploy. We review your setup before we quote.

Works with

Evaluate with

Overview

Size
524 MB
Weights
142 MB (safetensors)
Parameters
71M
Formats
onnx, safetensors
Task
text ranking
License
Apache-2.0 (permissive)
Files
13, each with a sha256
Publisher
mixedbread-ai
Added
2026-11-14
Section
alt.freeweights.embed
Lineage

Original release from the publisher.

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Model card

From the publisher at commit b5c6e9d. Shown as plain text with links; images open at the publisher.

The crispy rerank family from Mixedbread.

🍞 Looking for a simple end-to-end retrieval solution? Meet Omni, our multimodal and multilingual model. Get in touch for access.

mxbai-rerank-xsmall-v1#

This is the smallest model in our family of powerful reranker models. You can learn more about the models in our blog post.

We have three models:

Quickstart#

Currently, the best way to use our models is with the most recent version of sentence-transformers.

pip install -U sentence-transformers

Let's say you have a query, and you want to rerank a set of documents. You can do that with only one line of code:

from sentence_transformers import CrossEncoder

# Load the model, here we use our base sized model
model = CrossEncoder("mixedbread-ai/mxbai-rerank-xsmall-v1")


# Example query and documents
query = "Who wrote 'To Kill a Mockingbird'?"
documents = [
    "'To Kill a Mockingbird' is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.",
    "The novel 'Moby-Dick' was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.",
    "Harper Lee, an American novelist widely known for her novel 'To Kill a Mockingbird', was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.",
    "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.",
    "The 'Harry Potter' series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.",
    "'The Great Gatsby', a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan."
]

# Lets get the scores
results = model.rank(query, documents, return_documents=True, top_k=3)

JavaScript Example

Install transformers.js

npm i @xenova/transformers

Let's say you have a query, and you want to rerank a set of documents. In JavaScript, you need to add a function:

import { AutoTokenizer, AutoModelForSequenceClassification } from '@xenova/transformers';

const model_id = 'mixedbread-ai/mxbai-rerank-xsmall-v1';
const model = await AutoModelForSequenceClassification.from_pretrained(model_id);
const tokenizer = await AutoTokenizer.from_pretrained(model_id);

/**
 * Performs ranking with the CrossEncoder on the given query and documents. Returns a sorted list with the document indices and scores.
 * @param {string} query A single query
 * @param {string[]} documents A list of documents
 * @param {Object} options Options for ranking
 * @param {number} [options.top_k=undefined] Return the top-k documents. If undefined, all documents are returned.
 * @param {number} [options.return_documents=false] If true, also returns the documents. If false, only returns the indices and scores.
 */
async function rank(query, documents, {
    top_k = undefined,
    return_documents = false,
} = {}) {
    const inputs = tokenizer(
        new Array(documents.length).fill(query),
        {
            text_pair: documents,
            padding: true,
            truncation: true,
        }
    )
    const { logits } = await model(inputs);
    return logits
        .sigmoid()
        .tolist()
        .map(([score], i) => ({
            corpus_id: i,
            score,
            ...(return_documents ? { text: documents[i] } : {})
        }))
        .sort((a, b) => b.score - a.score)
        .slice(0, top_k);
}

// Example usage:
const query = "Who wrote 'To Kill a Mockingbird'?"
const documents = [
    "'To Kill a Mockingbird' is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.",
    "The novel 'Moby-Dick' was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.",
    "Harper Lee, an American novelist widely known for her novel 'To Kill a Mockingbird', was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.",
    "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.",
    "The 'Harry Potter' series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.",
    "'The Great Gatsby', a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan."
]

const results = await rank(query, documents, { return_documents: true, top_k: 3 });
console.log(results);

Using API#

You can use the large model via our API as follows:

from mixedbread_ai.client import MixedbreadAI

mxbai = MixedbreadAI(api_key="{MIXEDBREAD_API_KEY}")

res = mxbai.reranking(
  model="mixedbread-ai/mxbai-rerank-large-v1",
  query="Who is the author of To Kill a Mockingbird?",
  input=[
    "To Kill a Mockingbird is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.",
    "The novel Moby-Dick was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.",
    "Harper Lee, an American novelist widely known for her novel To Kill a Mockingbird, was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.",
    "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.",
    "The Harry Potter series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.",
    "The Great Gatsby, a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan."
  ],
  top_k=3,
  return_input=false
)

print(res.data)

The API comes with additional features, such as a continous trained reranker! Check out the docs for more information.

Evaluation#

Our reranker models are designed to elevate your search. They work extremely well in combination with keyword search and can even outperform semantic search systems in many cases.

ModelNDCG@10Accuracy@3
Lexical Search (Lucene)38.066.4
BAAI/bge-reranker-base41.666.9
BAAI/bge-reranker-large45.270.6
cohere-embed-v3 (semantic search)47.570.9
mxbai-rerank-xsmall-v143.970.0
mxbai-rerank-base-v146.972.3
mxbai-rerank-large-v148.874.9

The reported results are aggregated from 11 datasets of BEIR. We used Pyserini to evaluate the models. Find more in our blog-post and on this spreadsheet.

Community#

Please join our Discord Community and share your feedback and thoughts! We are here to help and also always happy to chat.

Citation#

@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},
}

License#

Apache 2.0

Files

13 files · 524 MBWeights 142 MB (safetensors). Sizes and sha256 from the signed manifest.
Hide all 13 files with their sha256
FileSizesha256
.gitattributes1.52 KB11ad7efa24975ee4b0c3c3a38ed18737f0658a5f75a0a96787b576a78a023361
LICENSE10.8 KB4b0dfefcb74f1e50a8df72a9f2bf0088753f8568bc479387292469b4948705d4
README.md49.5 KB7d0553b55a91f07a820125a5f4ec547489e50493fa2d2d6624fcc0f5725d1c7f
added_tokens.json23 Bdc046d04c9b0ada7ae6f1dc89c465801799acdf0c9a6aab8c15a1b2d5ca4e91f
config.json968 B470a53befc79da411cc04e466770d9f219f3c14adb276bfa0a58df28774ceade
model.safetensors142 MBa29bc212faf59c136ad0fd5712ecd2346e7b32c44a25b690625bc9ecebb14b8f
onnx/model.onnx284 MB536b7183f8964dd212c4e2f4029fcc595cfc03ac233aba03d293546aaa5a875b
onnx/model_quantized.onnx87.2 MB15ef19a6de90be7d52b627f2c784107bd806e64826450f41fb75fa4f0179ab30
onnx/quantize_config.json1.19 KBef9c74e2f5479dec41d6e82e49ee4b7cb3632c023cfa3cc4dc5032776fc4ba49
special_tokens_map.json970 Bb2f1b2f15f29a6b6d9d6ea4eca1675d2c231a71477f151d48f79cc83a625ba21
spm.model2.46 MBc679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
tokenizer.json8.65 MB305674b4d785287feecfb5f73f24aa75e9b57c87c579cfe24fbd207987d4b4c4
tokenizer_config.json1.45 KBaafc9f36a056307bf0cbfcbd42fe00d9df89083d23db6114466c8bfaedb09ce5
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
b5c6e9da73 pinned now manifest updated, same commit2026-11-14 21:10 UTC13 files · 524 MB
b5c6e9da732026-11-14 21:10 UTC13 files · 524 MB

Use it

$ fw get mixedbread-ai/mxbai-rerank-xsmall-v1
# manifest sha256 c0a034b70074… · 524 MB · 13 files
$ fw verify ./mxbai-rerank-xsmall-v1 mixedbread-ai/mxbai-rerank-xsmall-v1
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/mixedbread-ai/mxbai-rerank-xsmall-v1.json --dir ./mxbai-rerank-xsmall-v1 $ python3 fw.py verify ./mxbai-rerank-xsmall-v1 ./mixedbread-ai__mxbai-rerank-xsmall-v1.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download mixedbread-ai/mxbai-rerank-xsmall-v1 --revision b5c6e9da73abc3711f593f705371cdbe9e0fe422 --local-dir ./mxbai-rerank-xsmall-v1

License

Name
Apache License 2.0
Id
Apache-2.0
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
License as declared by the publisher. No person read it. 2026-11-14
Text in
LICENSE
Text sha256
4b0dfefcb74f1e50a8df72a9f2bf0088753f8568bc479387292469b4948705d4
Terms gate
No extra terms beyond the license.

Read the license text

Licenses are checked against the license table. A custom license passes only after a person reads it.

Provenance

From: mixedbread-ai <huggingface.co/mixedbread-ai>Newsgroups: alt.freeweights.embedSubject: mxbai-rerank-xsmall-v1Date: 2026-11-14Commit: b5c6e9dSigned: 2DC28CC7B26A283B ✓
Upstream
mixedbread-ai/mxbai-rerank-xsmall-v1
Pinned commit
b5c6e9da73abc3711f593f705371cdbe9e0fe422
Manifest
mxbai-rerank-xsmall-v1.json · .minisig
Manifest sha256
c0a034b700747038599e8c5ca65075c289a3073497091fe3d97725abd9f5758c
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-14 21:10 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/mixedbread-ai/mxbai-rerank-xsmall-v1.json $ curl -O https://freeweights.allternit.com/manifests/mixedbread-ai/mxbai-rerank-xsmall-v1.json.minisig $ minisign -Vm mxbai-rerank-xsmall-v1.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq $ shasum -a 256 <file> # compare with the Files tab

A manifest whose signature fails must be rejected. A file whose size or sha256 does not match must be discarded. Verify a download

Model tree

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Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download mixedbread-ai/mxbai-rerank-xsmall-v1
Open in Freeweights Studio

Pull, verify and run it in the Allternit app on Desktop, web or phone.

Signed manifest
minisign, key 2DC28CC7B26A283B. mxbai-rerank-xsmall-v1.json. Provenance. Takedown history: none.
From: sentence-transformers <huggingface.co/sentence-transformers>Subject: all-MiniLM-L6-v1
From: unsloth <huggingface.co/unsloth>Subject: all-MiniLM-L6-v2
From: mlx-community <huggingface.co/mlx-community>Subject: all-MiniLM-L6-v2-4bit
From: sentence-transformers <huggingface.co/sentence-transformers>Subject: all-mpnet-base-v1
From: sentence-transformers <huggingface.co/sentence-transformers>Subject: allenai-specter
From: sentence-transformers <huggingface.co/sentence-transformers>Subject: bert-base-nli-max-tokens