Laya Multilingual

Non-autoregressive System 1 decision model covering 100+ languages. Give it a state (text, email, ticket, or JSON) and typed questions; it returns typed answers with probabilities in a single forward pass. No text generation, so nothing to parse and nothing to hallucinate.

Part of the Laya family — use this checkpoint for anything that is not English.

Quickstart

pip install laya

import laya

agent = laya.load("convaiinnovations/laya-multilingual")
result = agent.predict(
    {"body": "मुझसे इनवॉइस 4411 के लिए दो बार शुल्क लिया गया। कृपया आज ही धनवापसी करें।"},
    {"department": {"type": "choice", "instructions": "Which team should handle `body`?",
                    "criteria": {"billing": "invoices, payments, refunds",
                                 "technical": "bugs and outages", "sales": "pricing"}},
     "refund_requested": {"type": "noul", "instructions": "Does the sender ask for money back?"}},
)
print(result["answers"]["department"]["choice"])      # billing

Let the Router choose

from laya import Router

router = Router()
router.predict({"body": "I was charged twice"}, questions)          # -> laya
router.predict({"body": "二重に請求されました"}, questions)            # -> laya-multilingual

Routing is per request, so a mixed workload otherwise pays a checkpoint swap on every language change. Load the ones you expect once, up front, and every later call is just a forward pass:

router = Router()
router.preload(["english", "multilingual"])      # both resident; no swap at request time

router.attach("multilingual", agent) registers an Agent you already built, so a process that loaded this checkpoint directly can hand it to the router instead of loading it twice.

Routing is decided from the script of the input, before the forward pass — because the model's confidence gives no warning when a checkpoint cannot read its input (see below).

If laya.load() hangs: transformers probes for TensorFlow at import, and when TF is installed its abseil runtime can deadlock model construction. Run with USE_TF=0.

Why this checkpoint exists

Measured across all 51 MASSIVE languages, intent classification with 20 options (random = 0.050), both checkpoints answering byte-identical questions:

The English checkpoint does not degrade gracefully outside English — it collapses, and stays confident while doing so. Khmer: 0.000 accuracy at 0.952 confidence. Hebrew 0.060, Armenian 0.050 (exactly random), Bengali 0.080 — all reported with 0.89–0.96 confidence. Its mean confidence never drops below 0.885 at any accuracy level, so confidence gating cannot catch it.

Per-language, this checkpoint turns near-random into usable: Arabic 0.110 → 0.400, Bengali 0.080 → 0.290, Azerbaijani 0.100 → 0.300, Hindi 0.100 → 0.387, Korean 0.110 → 0.490, Turkish 0.140 → 0.437.

XNLI (15 languages)

Speed — it is also the faster checkpoint

103–332 questions/sec batched on one T4, despite a 256k vocabulary — the 768-dim / 22-layer encoder is cheaper per token than 1024-dim / 28-layer, and the gap widens with batch size.

Architecture

• Backbone mmBERT-base (307M, bidirectional, 22 layers, hidden 768, 256k vocab) + a decision head trained from scratch: 2 transformer layers, an option-marker scorer, and an act/escalate head. 322M total.
• Option markers every option is scored at its own [MASK] token, then softmaxed over that question's options — so the answer space is defined per request, with no retraining.
• Budget 1024 tokens per question, of which 256 go to the question and its options.
• Trained from scratch with RLCD: 15,987 updates, 4 epochs, ~4.97 h.

Limits

• Ships uncalibrated. temperature = [1.0, 1.0, 1.0] with no per-option-count buckets. It is systematically over-confident (mean confidence 0.75–0.83 against much lower accuracy). Refitting one temperature per (question type, option count) on held-out data moves mean ECE 0.314 → 0.106. Do this on your own data before trusting the probabilities.
• Weaker on English than the English checkpoint: 0.619 vs 0.684 macro across English suites. Route rather than replace.
• Near chance on typed-decisions zero-shot — 0.342, against a 0.318 random and 0.461 majority-class baseline. Fine-tune for a specific workflow; that is where the capability comes from.
• Keep choice questions under ~20 options. Options share the fixed 256-token head budget, so a very large label space leaves only a few tokens per label and accuracy falls off sharply.
• Low-resource languages are weak, not fixed: Swahili 0.210, Tamil 0.250, Amharic 0.110.
• Ordinal score questions are the weakest primitive (SST-5 0.282).

Links

• Hub / family https://huggingface.co/convaiinnovations/laya
• GitHub https://github.com/NandhaKishorM/laya · full benchmark data on the research branch
• PyPI https://pypi.org/project/laya/
• Demo https://huggingface.co/spaces/convaiinnovations/laya-demo

Apache 2.0 · Convai Innovations