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Open-weight model · Text classification

laya-triage-multilingual

by Ignacio Joaquin Sanga Olmos elnachto/laya-triage-multilingual

laya-triage-multilingual is an open-weight model for text classification from Ignacio Joaquin Sanga Olmos, released under Apache License 2.0. It has 322M parameters. At 16-bit it needs about 0.8 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.

Classifies GitHub issues written in any language as bug, feature, question or docs. A fine-tune of Laya multilingual (mmBERT-base) used by the laya-triage GitHub Action for non-English issues, next to the English model laya-triage-en.

Parameters322M
Context—
Weights643.8 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—

Runs On

What it takes to serve laya-triage-multilingual (322M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 0.6 GB 0.8 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 0.3 GB 0.4 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 0.2 GB 0.2 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 1, 2026.

laya-triage-multilingual on every accelerator the SAVRN Index prices, at every precision

Model Card

By Ignacio Joaquin Sanga Olmos, published under apache-2.0, revision cdb5ce1293d2.

Classifies GitHub issues written in any language as bug, feature, question or docs. A fine-tune of Laya multilingual (mmBERT-base) used by the laya-triage GitHub Action for non-English issues, next to the English model laya-triage-en. The same 500 NLBSE'23 validation issues, machine-translated with NLLB-200 into 13 languages. Accuracy (±3 points per language): laya-triage and Jev are within noise of each other across languages; both are far ahead of the untuned base. Translations can flatter a model trained on translations, so we also checked real issues: on 367 non-English issues opened in 2026 (never seen, written by people, not translated) accuracy went from 47.1% to 65.7%. Use it…

Read Ignacio Joaquin Sanga Olmos's full model card

Classifies GitHub issues written in any language as bug, feature, question or docs. A fine-tune of Laya multilingual (mmBERT-base) used by the laya-triage GitHub Action for non-English issues, next to the English model laya-triage-en.

Results

The same 500 NLBSE'23 validation issues, machine-translated with NLLB-200 into 13 languages. Accuracy (±3 points per language):

Language Laya base Jev (TypeSafe, hosted) laya-triage
English 89.2% 88.2% 90.6%
German 74.6% 85.8% 87.4%
Vietnamese 71.2% 84.4% 86.6%
Chinese 68.2% 83.8% 86.2%
Japanese 67.8% 84.4% 86.0%
Turkish 68.2% 85.0% 85.8%
Indonesian 73.8% 86.0% 85.6%
Spanish 73.8% 85.6% 85.2%
Hindi 66.2% 85.4% 85.0%
Portuguese 74.0% 86.6% 84.8%
Russian 70.8% 85.8% 84.6%
Korean 64.2% 83.8% 84.4%
French 71.6% 85.2% 84.2%
Arabic 67.4% 84.8% 84.0%

laya-triage and Jev are within noise of each other across languages; both are far ahead of the untuned base.

Translations can flatter a model trained on translations, so we also checked real issues: on 367 non-English issues opened in 2026 (never seen, written by people, not translated) accuracy went from 47.1% to 65.7%.

How to use

Use it through the Laya router together with the English model, with the exact training question. See laya-triage-en for the code; this checkpoint is the "multilingual" entry.

Training

  • Base: convaiinnovations/laya-multilingual (mmBERT-base, 322M parameters).
  • Data: 2,000 NLBSE'23 training issues (500 per class) translated into 13 languages with NLLB-200 distilled 600M, plus the English originals and 10,000 extra English issues: 35,200 examples. Code blocks, stack traces and error messages were left untranslated, as in real issues.
  • Validation: 200 issues held out in all 14 languages (no issue appears in both splits in any language).
  • One epoch, same recipe as the English model; the second epoch overfit and was discarded.
  • Calibration: temperature 1.203 (the base model was overconfident), ECE 0.077 → 0.053.

Limitations

  • Machine-translated training data: real issues mix languages, code and English error messages more than translations do.
  • 13 languages measured; others are supported by the base model but not evaluated.
  • Same class-prior note as the English model.

Credits

Base model by Convai Innovations (Apache-2.0). Translation with NLLB-200. Data from the NLBSE'23 tool competition. Built by elnachto.

Identity and Version

Repository
elnachto/laya-triage-multilingual
Publisher
Ignacio Joaquin Sanga Olmos
Task
Text classification
Modality
Text
Library
Not stated by the source
Parameters
322M parameters
Languages
en, de, es, pt, fr, id, vi, ru
Revision
cdb5ce1293d2c570af22bfb3af406535da6906ae
First published
2026-09-28
Last updated
2026-09-28

Files and Weights

7 files, 678.2 MB in total. The weights are 1 file totalling 643.8 MB in safetensors.

Weights1 file · 643.8 MB
Configuration2 files · 2.7 KB
Tokenizer2 files · 34.4 MB
Documentation1 file · 3.3 KB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
model.safetensorsWeights643.8 MB 67429ad745be
encoder/config.jsonConfiguration1.9 KB —
rl_agent_config.jsonConfiguration739 B —
README.mdDocumentation3.3 KB —
.gitattributesRepository1.6 KB —
tokenizer/tokenizer.jsonTokenizer34.4 MB 609d8f4c067c
tokenizer/tokenizer_config.jsonTokenizer545 B —

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
643.8 MB
Download from Ignacio Joaquin Sanga Olmos

Released by Ignacio Joaquin Sanga Olmos through its official repository on Hugging Face. Read the license.

Built From

Memory Requirements

PrecisionWeights in memory
As published643.8 MB
16-bit0.6 GB
8-bit0.3 GB
4-bit0.2 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About laya-triage-multilingual

How much GPU memory does laya-triage-multilingual need?

About 0.8 GB at 16-bit and 0.2 GB at 4-bit: the weights (322M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run laya-triage-multilingual on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use laya-triage-multilingual commercially?

Yes. laya-triage-multilingual is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

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