alt.freeweights.embed / gte-multilingual-base
From: onnx-community <huggingface.co/onnx-community>Newsgroups: alt.freeweights.embedSubject:

gte-multilingual-base

Date: 2026-11-14Commit: 2edbf5eSigned: 2DC28CC7B26A283B ✓

Feature extraction model. Onnx weights, 5.13 GB in all. Published by onnx-community under Apache-2.0.

embed5.13GBgte-multilingual-baseonnxpermissive
FactsParamsnot statedSize5.13 GBFormatsonnxTaskfeature extractionLicenseApache-2.0From sourcepermissive
$ fw get onnx-community/gte-multilingual-base
# manifest sha256 d49880f0509a… · 5.13 GB · 15 files
$ fw verify ./gte-multilingual-base onnx-community/gte-multilingual-base
From sourceHosted (signed)onnx-community/gte-multilingual-baseLast check: 2026-11-14 21:31 UTC

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

Quantized of gte-multilingual-base.

Works with

Runs with

Ranked by fit to this model (onnx), then by GitHub stars.

Convert with

Evaluate with

Scan with

Overview

Size
5.13 GB
Weights
340 MB (smallest of 8 ONNX variants)
Parameters
not stated
Formats
onnx
Task
feature extraction
License
Apache-2.0 (permissive)
Files
15, each with a sha256
Publisher
onnx-community
Added
2026-11-14
Section
alt.freeweights.embed
Lineage

Original release from the publisher.

Base model: Alibaba-NLP/gte-multilingual-base

Where these bytes come from
Publisher at the pinned commit→You

Sources are tried in order. A file counts only when its size and sha256 match the manifest. Sources are checked when you ask for them, not on a timer.

Sources
BadgeHostLast checkResult
From sourcehuggingface.co2026-11-14 21:31 UTCpassed

Model card

From the publisher at commit 2edbf5e. Shown as plain text with links; images open at the publisher.

https://huggingface.co/Alibaba-NLP/gte-multilingual-base with ONNX weights to be compatible with Transformers.js.

Usage (Transformers.js)#

If you haven't already, you can install the Transformers.js JavaScript library from NPM using:

npm i @huggingface/transformers

You can then use the model to compute embeddings, as follows:

import { pipeline } from '@huggingface/transformers';

// Create a feature-extraction pipeline
const extractor = await pipeline('feature-extraction', 'onnx-community/gte-multilingual-base');

// Compute sentence embeddings
const texts = ['Hello world.', 'Example sentence.'];
const embeddings = await extractor(texts, { pooling: 'mean', normalize: true });
console.log(embeddings);
// Tensor {
//   dims: [ 2, 768 ],
//   type: 'float32',
//   data: Float32Array(1536) [ 0.019079938530921936, 0.041718777269124985, ... ],
//   size: 1536
// }

console.log(embeddings.tolist()); // Convert embeddings to a JavaScript list
// [
//   [ -0.04247443005442619, 0.00007914059096947312, -0.007467088755220175, ... ],
//   [ -0.05675575137138367, 0.0288529209792614, -0.02864679880440235, ... ]
// ]

You can also use the model for retrieval. For example:

import { pipeline, cos_sim } from '@huggingface/transformers';

// Create a feature-extraction pipeline
const extractor = await pipeline('feature-extraction', 'onnx-community/gte-multilingual-base');

// List of documents you want to embed
const texts = [
    'Hello world.',
    'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.',
    'I love pandas so much!',
];

// Compute sentence embeddings
const embeddings = await extractor(texts, { pooling: 'mean', normalize: true });

// Prepend recommended query instruction for retrieval.
const query_prefix = 'Represent this sentence for searching relevant passages: '
const query = query_prefix + 'What is a panda?';
const query_embeddings = await extractor(query, { pooling: 'mean', normalize: true });

// Sort by cosine similarity score
const scores = embeddings.tolist().map(
    (embedding, i) => ({
        id: i,
        score: cos_sim(query_embeddings.data, embedding),
        text: texts[i],
    })
).sort((a, b) => b.score - a.score);
console.log(scores);
// [
//   { id: 1, score: 0.8908273895482127, text: 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.' },
//   { id: 2, score: 0.7903781165100383, text: 'I love pandas so much!' },
//   { id: 0, score: 0.7320514921911025, text: 'Hello world.' }
// ]

Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).

Files

15 files · 5.13 GBWeights 340 MB (smallest of 8 ONNX variants). Sizes and sha256 from the signed manifest.
Hide all 15 files with their sha256
FileSizesha256
.gitattributes1.57 KB34448b82c17d60fec9b65b1f093c115ddbaadc04beb1b0140b6bfed2e012a930
README.md3.19 KB442e2f38f153f2ca16040f1c5c47c025d0198f3a1ae6603e4b7a25070154d874
config.json1.65 KB6ef2538d4286a7cd18d05225f659d8a1bceca7adb01c186868e53dbd4f822e17
onnx/model.onnx1.26 GB5b9f03fdc40350a78fa064b4cfb6bf9a229a7c40aa87736f537e3ebd00aa2b86
onnx/model_bnb4.onnx866 MBf66b74eec8cffc78401e0ce6148d00c3986b2af4fa053d9c35607ab6b2825e53
onnx/model_fp16.onnx628 MBf1d0f4ec988a6c17387d3b256e631deea506a891aed3a6ded4f9bf09386cc38e
onnx/model_int8.onnx340 MBab2bd164ebd8ca9003dc49a981b611e849b5d326f504c8873ba76e07fa6c0082
onnx/model_q4.onnx873 MB27c503475e420e61b2cc43fc247fb55f7aba65ad462a10dde168ded281069561
onnx/model_q4f16.onnx465 MB9726ec27b72918ca0ed491f7f28bb52e6a5c7b8e47a49bd5f6c08a93675a6b55
onnx/model_quantized.onnx340 MBab2bd164ebd8ca9003dc49a981b611e849b5d326f504c8873ba76e07fa6c0082
onnx/model_uint8.onnx340 MB9524e1fc6afb876b2fe3578f7dc174770865fce4d5d0d22d3d5e03617e0e73cc
quantize_config.json283 Be88beb3a98997ba54ba68adb41f91f3572fb0457a4d7c5cb943d3b9e78644fff
special_tokens_map.json964 B8c785abebea9ae3257b61681b4e6fd8365ceafde980c21970d001e834cf10835
tokenizer.json17.1 MB3a56def25aa40facc030ea8b0b87f3688e4b3c39eb8b45d5702b3a1300fe2a20
tokenizer_config.json1.15 KB24cebbf2ef20fc317256e03e52ac7b2ca326586f946a8427ecac036332bf0933
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
2edbf5e672 pinned now manifest updated, same commit2026-11-14 21:31 UTC15 files · 5.13 GB
2edbf5e6722026-11-14 21:31 UTC15 files · 5.13 GB

Use it

$ fw get onnx-community/gte-multilingual-base
# manifest sha256 d49880f0509a… · 5.13 GB · 15 files
$ fw verify ./gte-multilingual-base onnx-community/gte-multilingual-base
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/onnx-community/gte-multilingual-base.json --dir ./gte-multilingual-base $ python3 fw.py verify ./gte-multilingual-base ./onnx-community__gte-multilingual-base.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download onnx-community/gte-multilingual-base --revision 2edbf5e672aab465f9ed4c154a8b61791c082c69 --local-dir ./gte-multilingual-base

License

Name
Apache License 2.0
Id
Apache-2.0
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
Inherited from the base model Alibaba-NLP/gte-multilingual-base: the strictest license along the lineage applies. 2026-11-14
Text in
README.md
Text sha256
442e2f38f153f2ca16040f1c5c47c025d0198f3a1ae6603e4b7a25070154d874
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: onnx-community <huggingface.co/onnx-community>Newsgroups: alt.freeweights.embedSubject: gte-multilingual-baseDate: 2026-11-14Commit: 2edbf5eSigned: 2DC28CC7B26A283B ✓
Upstream
onnx-community/gte-multilingual-base
Pinned commit
2edbf5e672aab465f9ed4c154a8b61791c082c69
Manifest
gte-multilingual-base.json · .minisig
Manifest sha256
d49880f0509a13ad62f0fd8d130541f69ff07d56f5182c180552cd806b861da3
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-14 21:31 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/onnx-community/gte-multilingual-base.json $ curl -O https://freeweights.allternit.com/manifests/onnx-community/gte-multilingual-base.json.minisig $ minisign -Vm gte-multilingual-base.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

Parent

Alibaba-NLP/gte-multilingual-base · this model is a quantization of it

No model in the catalog names this one as its parent.

Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download onnx-community/gte-multilingual-base
Open in Freeweights Studio

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

Signed manifest
minisign, key 2DC28CC7B26A283B. gte-multilingual-base.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