Model reference · open weights

mxbai-rerank-xsmall

Embeddings mixedbread-ai Reranker 1 build Open weights 580k dl/mo

mxbai-rerank-xsmall is an open-weight embedding model from mixedbread-ai. mxbai-rerank-xsmall-v1 (FP16) weighs 142 MB; the smallest configuration that runs it is RTX 3060 12 GB.

  • mxbai-rerank-xsmall is a text-ranking model developed by mixedbread-ai for reranking documents based on a query.
  • It contains 71 million parameters, supports a context length of 512 tokens, and is designed for English language tasks.
  • The model is released under the Apache 2.0 license.

Summary of the mixedbread-ai/mxbai-rerank-xsmall-v1 model card, 2026-10-01

What it is

Released bymixedbread-ai
Released2024-02-29
Parameters71M
VRAM142 MB for the weights

What it runs on

Memory and cards for mxbai-rerank-xsmall-v1 (FP16)

142 MBweights, file size
1.1 GBruntime overhead
CardRunsMemory
RTX 3060 12 GBfits11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What mixedbread-ai says about mxbai-rerank-xsmall

Read the model card

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)

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 m

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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