Text analysisSearch and RAG

mmBERT

A multilingual encoder trained on more than 1800 languages. The list includes Tatar, Bashkir, Chuvash, Udmurt, Buryat, Komi, Ingush and other languages of Russia. A base for classifiers and search over such texts.

Developer
Johns Hopkins University (JHU CLSP), USA
First release
Jul 2025
Latest release
Aug 2025
Sizes
140M and 307M
License
Commercial use allowedMIT
Russian
Supported
Ready-made builds
GGUF
Running
On your own serverAlso runs without a GPU
Industries
Documents and accounting, Customer support, Science and research, Media and production
Deploy on your server Hugging Face GitHub

What it does

  • Classifying requests and documents in national languages
  • Semantic search over texts in rare languages
  • Extracting names, dates and titles from text
  • A base for fine-tuning to your own task

Where it is used

Document flow and archivesCustomer supportResearch and language projects

Hardware requirements

LaptopLaptop or regular PC, up to 8 GB of VRAM — smaller versions
fits
1 GPUOne GPU with 16–80 GB — mid-size versions
no versions
ClusterServer with several GPUs — flagship versions
no versions

Versions

  1. mmBERT-small, 140M
  2. mmBERT-base, 307M

How to run it

On your own serverWeights are downloaded from Hugging Face and served with vLLM (load and API) or llama.cpp (modest hardware). The model then runs in a closed network with no per-request fees.Hugging Face
How much hardware you needCalculate the VRAM for the model size, context length and number of concurrent requests.Open the hardware calculator

I can set this up end to end: pick the model size, deploy it on your server and connect it to your systems. Quantization compresses a model so it takes less video memory and runs on more modest hardware. Answers change slightly, so quality is checked on your own examples.

Frequently asked questions

Can mmBERT be used in a commercial project?

Yes. License: MIT. It allows commercial use, but it is still worth having a lawyer review the license before launch.

What hardware does mmBERT need?

At minimum: Laptop or regular PC, up to 8 GB of VRAM — smaller versions. Some versions also run on an ordinary CPU, without a GPU. You can calculate the exact VRAM for your model size and context in the hardware calculator.

Does mmBERT support Russian?

Yes, Russian is listed on the model card.

Where can I download mmBERT and what does it cost?

The mmBERT weights are open and free to download. You only pay for the hardware it runs on and for the setup. Source links are at the bottom of this page.

How I deploy it for clients

  1. SelectionI pick the model size for your task and hardware and test it on your examples.
  2. DeploymentI deploy it on your server or in a closed network and provide an API.
  3. Fine-tuningI fine-tune it on your data (LoRA) or connect a knowledge base — whichever is cheaper for the task.
  4. IntegrationI connect it to your CRM, ERP, bot, website or team chat and set up monitoring.

Similar models

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DetailsText analysisModernBERTAnswer.AI and LightOn · USA / FranceCommercial use allowed

A modern replacement for classic BERT: faster, reads up to 8 thousand tokens at once. A base for your own classifiers. Trained on English and code; for Russian there is RuModernBERT.

DetailsText analysisGlotLIDCIS, LMU Munich · GermanyCommercial use allowed

Detects which language a text is written in: the third version covers more than two thousand labels, including Tatar, Bashkir, Chuvash, Udmurt, Mari, Erzya, Komi and other languages of Russia.

Details

Source: huggingface.co/jhu-clsp/mmBERT-base. Data checked against the model card on 22 Sep 2026. Have a lawyer review the license before commercial launch.