--- title: "Laya Multilingual" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/d866009d-522a-4712-bb8f-6721bfe5058c" updated: "2026-11-13" --- # Laya Multilingual > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads text such as emails and tickets in 100+ languages and answers typed questions with probabilities in one pass. A 322M model, with no text generation, for teams routing multilingual messages. Laya Multilingual is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by convaiinnovations ## About ### What it is Laya Multilingual is a decision model for text. You hand it a message, such as an email, a ticket or a JSON record, together with typed questions. One forward pass later, it gives back typed answers that each carry a probability. It does not write text, so there is nothing to parse. It has 322M parameters and covers 100+ languages. ### Who it suits Developers and support teams who sort messages in many languages and want fixed answers, not free text. The card points text in any other language to this checkpoint. The English checkpoint is a separate model in the same family. Who uses it, and how | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Customer support | Support teams | Route tickets to the right team | Send a refund complaint in Hindi to billing | | Operations | Back-office teams | Tag incoming mail by topic or urgency | Mark which emails ask for money back | | Software | Developers | Add a small classifier to an app | Call the model once per message and read the label | | Localization | Global companies | Handle many languages with one model | Sort mixed Spanish and Japanese tickets together | Skip it if you need the model to write replies or summaries, because it only returns labels and probabilities. ### Where to use it - Python, with the laya package - Servers that sort a stream of messages - Windows, macOS or Linux for downloading the files, with Python | Use case | Who | What they get back | | --- | --- | --- | | Ticket routing | Support teams | A chosen team name and its probability | | Yes or no checks | Operations | A typed answer, such as whether the sender wants a refund | | Long documents | Developers | Answers for text up to 8,192 tokens when you raise the limit | ### How to set it up You need Python and pip. Install the laya package, which loads the model by its Hugging Face name. You can also download the files by hand. - Python and pip - The laya package - About 644 MB of disk space for the F16 weights, per Hugging Face 1. Install the laya package. 2. Load the multilingual checkpoint by name. 3. Pass your text and typed questions to predict. ```bash pip install laya pip install -U huggingface_hub hf download convaiinnovations/laya-multilingual ``` If loading hangs, the card says transformers may probe for TensorFlow and deadlock, and running with USE\_TF=0 avoids it. ### How to use it Pass the text and a set of typed questions to predict. A choice question lists the options and what each means. For long documents, set max\_len to 8192, because the default limit of 1,024 tokens cuts them off. ```python import laya agent = laya.load("convaiinnovations/laya-multilingual") result = agent.predict(long_document, questions, max_len=8192) ``` The card also describes a Router helper. It looks at the script the input is written in, before the model runs, and picks the English or multilingual checkpoint. You can preload both at startup so the first request is quick. ```python from laya import Router router = Router() router.preload(["english", "multilingual"]) ``` | Tool | What it does | | --- | --- | | laya.load | Loads a checkpoint by its Hugging Face name | | predict | Takes text and typed questions, returns answers with probabilities | | max\_len | Raises the input limit from 1,024 to 8,192 tokens | | Router | Sends each message to the English or multilingual checkpoint | ### Example You pass a billing complaint in Hindi, plus a choice question with billing, technical and sales as options. The model returns billing with a probability, in one pass. You read the label and route the ticket. ### For newcomers and experts If you are new, start with one choice question on a handful of messages and read the probabilities. Check them against answers you know before you automate anything. For people in the field, the encoder is mmBERT-base, with a 1,024-token context that extends to 8,192. The card says short inputs give identical answers with the larger limit. Its long-document test shows 16 to 18 of 20 correct up to about 4,000 tokens and 8 to 17 of 20 beyond that. Text arrives → Router checks the script → Laya reads text and questions → One pass returns typed answers → Your code uses the label ### Good to know Macro accuracy across 51 languages | Label | Value | | --- | --- | | Random guess | 0.05 accuracy | | English checkpoint | 0.227 accuracy | | Multilingual checkpoint | 0.366 accuracy | - License: Apache 2.0 - Cost to run: none named, you use your own computer - Data it reaches: it downloads files from Hugging Face, and the card names nothing else it touches - Not for: writing text, or English-only work, where the English checkpoint is the one the card suggests - Test long-document accuracy on your own data, because the card reports uneven results past roughly 4,000 tokens ## README ````markdown # Laya Multilingual Reads text such as emails and tickets in 100+ languages and answers typed questions with probabilities in one pass. A 322M model, with no text generation, for teams routing multilingual messages. Made by convaiinnovations on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Laya Multilingual is a decision model for text. You hand it a message, such as an email, a ticket or a JSON record, together with typed questions. One forward pass later, it gives back typed answers that each carry a probability. It does not write text, so there is nothing to parse. It has 322M parameters and covers 100+ languages. Who it suits: Developers and support teams who sort messages in many languages and want fixed answers, not free text. The card points text in any other language to this checkpoint. The English checkpoint is a separate model in the same family. ```table Title: Who uses it, and how Industry | Who uses it | How they use it | Example Customer support | Support teams | Route tickets to the right team | Send a refund complaint in Hindi to billing Operations | Back-office teams | Tag incoming mail by topic or urgency | Mark which emails ask for money back Software | Developers | Add a small classifier to an app | Call the model once per message and read the label Localization | Global companies | Handle many languages with one model | Sort mixed Spanish and Japanese tickets together ``` Skip it if you need the model to write replies or summaries, because it only returns labels and probabilities. Where to use it: - Python, with the laya package - Servers that sort a stream of messages - Windows, macOS or Linux for downloading the files, with Python ```table Use case | Who | What they get back Ticket routing | Support teams | A chosen team name and its probability Yes or no checks | Operations | A typed answer, such as whether the sender wants a refund Long documents | Developers | Answers for text up to 8,192 tokens when you raise the limit ``` How to set it up: You need Python and pip. Install the laya package, which loads the model by its Hugging Face name. You can also download the files by hand. - Python and pip - The laya package - About 644 MB of disk space for the F16 weights, per Hugging Face 1. Install the laya package. 2. Load the multilingual checkpoint by name. 3. Pass your text and typed questions to predict. ```bash pip install laya pip install -U huggingface_hub hf download convaiinnovations/laya-multilingual ``` If loading hangs, the card says transformers may probe for TensorFlow and deadlock, and running with USE_TF=0 avoids it. How to use it: Pass the text and a set of typed questions to predict. A choice question lists the options and what each means. For long documents, set max_len to 8192, because the default limit of 1,024 tokens cuts them off. ```python import laya agent = laya.load("convaiinnovations/laya-multilingual") result = agent.predict(long_document, questions, max_len=8192) ``` The card also describes a Router helper. It looks at the script the input is written in, before the model runs, and picks the English or multilingual checkpoint. You can preload both at startup so the first request is quick. ```python from laya import Router router = Router() router.preload(["english", "multilingual"]) ``` ```table Tool | What it does laya.load | Loads a checkpoint by its Hugging Face name predict | Takes text and typed questions, returns answers with probabilities max_len | Raises the input limit from 1,024 to 8,192 tokens Router | Sends each message to the English or multilingual checkpoint ``` Example: You pass a billing complaint in Hindi, plus a choice question with billing, technical and sales as options. The model returns billing with a probability, in one pass. You read the label and route the ticket. For newcomers and experts: If you are new, start with one choice question on a handful of messages and read the probabilities. Check them against answers you know before you automate anything. For people in the field, the encoder is mmBERT-base, with a 1,024-token context that extends to 8,192. The card says short inputs give identical answers with the larger limit. Its long-document test shows 16 to 18 of 20 correct up to about 4,000 tokens and 8 to 17 of 20 beyond that. ```flow Text arrives Router checks the script Laya reads text and questions One pass returns typed answers Your code uses the label ``` Good to know: ```chart Title: Macro accuracy across 51 languages Random guess: 0.05 accuracy English checkpoint: 0.227 accuracy Multilingual checkpoint: 0.366 accuracy ``` - License: Apache 2.0 - Cost to run: none named, you use your own computer - Data it reaches: it downloads files from Hugging Face, and the card names nothing else it touches - Not for: writing text, or English-only work, where the English checkpoint is the one the card suggests - Test long-document accuracy on your own data, because the card reports uneven results past roughly 4,000 tokens ## On Hugging Face, 13 November 2026 - Task: text classification - Library: transformers - Parameters: 322M - Weights: about 644 MB in F16; about 161 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 9,698 - Likes: 422 - License: apache-2.0 - Languages: en, de, fr, es, pt, it, nl, sv - Quantized versions: 48 - Fine-tunes: 94 - Tags: transformers, safetensors, laya, multilingual, mmbert, system-one, calibrated-decisions, rlcd, classification, routing, guardrails, moderation ## Links - Model: https://huggingface.co/convaiinnovations/laya-multilingual - Files: https://huggingface.co/convaiinnovations/laya-multilingual/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download convaiinnovations/laya-multilingual ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [convaiinnovations/laya-multilingual on Hugging Face](https://huggingface.co/convaiinnovations/laya-multilingual) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Laya Multilingual (this page) | 9,698 | 422 | 322M | apache-2.0 | 2026-10-03 | | [Laya](https://mindket.com/p/df9dae6e-c168-4ebd-a371-bb1875b89c9b) | 18,689 | 5,956 | 421M | apache-2.0 | 2026-11-09 | | [elephant-1](https://mindket.com/p/ae806c31-0f69-4485-8475-443c9347b0a8) | 7,426 | 115 | 9.4B | mit | 2026-10-22 | | [mammoth-1](https://mindket.com/p/72aafbd9-0774-437a-9492-f659f9a2a186) | 5,903 | 97 | 27.4B | mit | 2026-10-22 | | [joey-1](https://mindket.com/p/1e3f2aa5-2042-45ae-a466-48b620b003f1) | 1,539 | 47 | 2.7B | mit | 2026-10-29 | | [FRIDA-Decisions](https://mindket.com/p/fe708cbf-5142-4b59-9e43-f0253217cc53) | 1,111 | 57 | 823M | mit | 2026-10-10 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-03 - First published: 2026-09-19 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open convaiinnovations/laya-multilingual on Hugging Face](https://huggingface.co/convaiinnovations/laya-multilingual) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/d866009d-522a-4712-bb8f-6721bfe5058c) - [Its page on Mindket](https://mindket.com/p/d866009d-522a-4712-bb8f-6721bfe5058c)