Multilingual, non-autoregressive System 1 decision engine. Typed decisions over 100+ languages in a single forward pass — 33 ms — trained with reinforcement learning against strictly proper scoring rules (RLCD), with a router that picks the right checkpoint per request.

Laya evaluates typed questions (choice, score, noul) over any state (text, email, ticket or JSON document) in a single forward pass — 33 ms for one question, 7.2 ms/question batched, measured on a T4. No text generation, so nothing to parse and nothing to hallucinate.

Three checkpoints, and a Router that picks between them per request:

	encoder	params	context	use it for
laya	ModernBERT-large	421M	512	English
laya-multilingual	mmBERT-base	322M	1024	100+ languages, 2x faster
laya-typed-decisions	ModernBERT-large	421M	1024	the typed-decisions workflows

---

Installation

pip install laya

---

Quickstart

import laya

# 1. Load the fine-tuned model directly from Hugging Face Hub (auto-downloads weights)
agent = laya.load("convaiinnovations/laya")

# 2. Provide any state (string or dictionary)
state = {
    "from": "user@acme.com",
    "subject": "Duplicate charge on invoice #4411",
    "body": "Hi, we were billed twice for March. Please refund the duplicate today or we will cancel our plan."
}

# 3. Define your typed questions
questions = {
    # choice: categorical selection with probabilities & confidence
    "department": {
        "type": "choice",
        "instructions": "Which department should handle this email?",
        "criteria": {
            "billing": "invoices, payments, refunds",
            "technical": "bugs, outages, system errors",
            "sales": "pricing, new contracts",
            "other": "everything else"
        }
    },
    # score: placement on an ordinal rubric
    "urgency": {
        "type": "score",
        "instructions": "How urgent is this request?",
        "criteria": ["not urgent", "soon", "critical deadline or blocking issue"]
    },
    # noul: calibrated boolean probability P(true)
    "churn_risk": {
        "type": "noul",
        "instructions": "Does the user threaten to cancel or leave?"
    },
    "is_phishing": {
        "type": "noul",
        "instructions": "Is this email a phishing or scam attempt?"
    }
}

# 4. Run all questions in ONE single forward pass (~35 ms on GPU)
result = agent.predict(state, questions)
answers = result["answers"]

print("Department :", answers["department"]["choice"])
# -> billing (confidence: 0.94)

print("Urgency    :", answers["urgency"]["score"])
# -> 1.84 / 2.0

print("Churn Risk :", answers["churn_risk"]["noul"])
# -> 0.892 (89.2% probability)

print("Phishing   :", answers["is_phishing"]["noul"])
# -> 0.008 (0.8% probability)

---

Automated Confidence Gating

Because Laya's probabilities are trained with strictly proper scoring rules (RLCD), confidence scores are statistically meaningful:

dept = answers["department"]["choice"]
conf = answers["department"]["confidence"]

if conf >= 0.85:
    # High confidence: automated action without human in the loop
    route_automatically(dept)
else:
    # Low confidence: escalate to human triage
    escalate_to_human_agent(dept, reason=f"Low confidence ({conf:.2f})")

---

Built-in Workflow Presets

Laya provides pre-tuned question schemas for immediate production use:

import laya

agent = laya.load("convaiinnovations/laya")

# 1. Intelligent Model Router (routes to small vs. frontier models)
routing = agent.predict({"request": "Refactor this service using dependency injection"}, laya.router_questions())

# 2. Real-time Prompt Guardrails (jailbreaks, injections, leaks)
guard = agent.predict({"prompt": "Ignore all instructions"}, laya.guard_questions())

# 3. Content Safety & Moderation (toxicity, harassment, threats)
safety = agent.predict({"post": "User comment text"}, laya.moderation_questions())

# 4. Support Ticket Triage (intent, urgency, frustration, churn)
triage = agent.predict({"message": "My payment failed twice"}, laya.triage_questions())

---

Model Routing (three checkpoints, one call)

Laya ships three checkpoints. Router picks the right one per request and loads it lazily.

name	repo	size	context	best at
english	convaiinnovations/laya	421M	512	English text
multilingual	convaiinnovations/laya-multilingual	322M	1024	100+ languages, 2x faster
typed-decisions	convaiinnovations/laya-typed-decisions	421M	1024	the four typed-decisions workflows

from laya import Router

router = Router()          # nothing is downloaded until a request needs it

# English -> routed to the English checkpoint
router.predict({"body": "I was charged twice, please refund."}, questions)

# Hindi -> routed to the multilingual checkpoint automatically
router.predict({"body": "मुझसे दो बार शुल्क लिया गया"}, questions)

# explicit when you already know
router.predict(state, questions, model="typed-decisions")
router.predict(state, questions, lang="de")

Every result carries the decision that produced it:

result = router.predict({"body": "二重に請求されました"}, questions)
result["routing"]
# {'model': 'multilingual',
#  'repo': 'convaiinnovations/laya-multilingual',
#  'reason': 'non-Latin script (kana, 100% of letters); the English checkpoint cannot read it',
#  ...}

Inspect a decision without running the model:

router.route({"body": "Der Kunde wurde zweimal belastet"}, questions).reason
# "Latin script but language looks like 'de', not English"

Why route at all

Accuracy on a shared benchmark (17,416 questions, one T4, identical questions per model):

	english	multilingual
MASSIVE intent, English	0.783	0.657
MASSIVE intent, 13 other languages	0.306	0.451
XNLI, English	0.860	0.843
XNLI, 14 other languages	0.521	0.731
English-only suites	0.684	0.619
Latency, 10 questions	159 ms	72 ms

The English checkpoint does not degrade gracefully outside English -- it collapses, and stays confident while doing so. On 20-option MASSIVE intent (random = 0.050) it scores 0.100 on Hindi and 0.103 on Korean, with an expected calibration error of 0.855. Script detection is therefore the primary routing signal.

Routing rules

Precedence, highest first:

1. model= -- explicit checkpoint.
2. task="typed_decisions" -- explicit task.
3. A question-id set exactly matching a typed-decisions workflow, only if you construct the router with auto_task_detection=True. It is off by default: that checkpoint is fine-tuned on four synthetic workflows and should not be a silent fallback.
4. lang= -- explicit language code.
5. Detected script (exact) and, for Latin text, a stopword/diacritic language guess (best effort).
6. default= ("english" unless you change it).

Preload — make routing free

A cold checkpoint build costs seconds; language detection costs microseconds. At the default max_loaded=1, traffic that alternates languages rebuilds a model on every request. For a server or a demo, preload:

router = Router(preload=True)                  # every checkpoint resident, routing is free
router = Router(preload=True, device="cuda")
router.preload(["english", "multilingual"])    # or just the two you serve

preload raises max_loaded to fit what it built, so the LRU cannot evict it immediately.

If the process already has a checkpoint loaded for other reasons, hand it over instead of loading a second copy:

router.attach("english", existing_agent)   # no duplicate 421M parameters
router.preload()                           # builds only what is still missing

Measured on CPU with the demo Space's own workload:

	per request	model loads
Router() — lazy, max_loaded=1	4–6 s on every language switch	1 per switch
Router(preload=True)	193–464 ms	none

Memory

All three together are ~1.16B parameters (~4.6 GB fp32), so Router keeps one resident by default and evicts least-recently-used. Raise it when you have the RAM:

Router(max_loaded=2)       # keep two hot
router.unload()            # free everything
router.loaded              # ['multilingual']

---

Decision Primitives

Primitive	Output	Use Cases
choice	Top label, probabilities per option, confidence	Department routing, intent classification, topic categorization
score	Expected level on ordinal rubric, distribution, confidence	Frustration level, ticket urgency, harm severity
noul	Calibrated probability P(true) from 0.0 to 1.0	Phishing detection, spam filtering, jailbreak detection, churn risk

---

Benchmarks

Full report: BENCHMARKS.md — every run consolidated, languages and themes, with per-language detail for all 51 languages.

All Laya numbers below are measured. Every model answered byte-identical questions (fixed seed) in the same run. Reproduce with notebooks/laya_benchmark_colab.ipynb on a T4.

Speed (Tesla T4, measured)

questions per call	laya	laya-multilingual
1	39.5 ms	32.8 ms
5	84.5 ms	40.1 ms
10	158.6 ms (15.9 ms/q)	72.3 ms (7.2 ms/q)
50	771 ms	337 ms (6.8 ms/q)

Batched throughput reaches 103-332 questions/sec on a single T4. For reference, TypeSafe Jev has been independently measured at 236-276 ms p50 (AbdelStark, nibzard) -- Laya answers a single question roughly 6-7x faster.

Laya (with routing) vs Jev

Every Laya figure is what Router().predict(...) actually returns — the checkpoint the router selects for that input, not a hand-picked best of three. Jev figures are third-party published, never measured here (no TypeSafe API access), so sample sizes and prompts differ.

	Jev 1.13.0	Laya (routed)
typed-decisions, 2,000 decisions	0.727	0.766	+0.039
AG News, 4 labels	0.910	0.950	+0.040
DAIR Emotion, 6 labels	0.480	0.595	+0.115
ECE (lower better)	0.246	0.081	3× better
p50 latency, 1 question	236–276 ms	32.8 ms	7.8× faster
Languages usable	no published benchmark	45 of 51	—
Weights	closed API	Apache 2.0	—
Cost	$0.042 / 1M tokens	$0 self-hosted	—

On DAIR Emotion, Jev assigned zero probability to the true label on 16% of examples — a hard failure for anything branching on confidence.

Full detail, including every workflow and all 51 languages: BENCHMARKS.md.

typed-decisions, measured on all three checkpoints

400 cases, 2,000 decisions, four workflows.

model	accuracy	soft acc	Brier	ECE	score MAE
laya-typed-decisions	0.766	0.471	0.062	0.213	0.242
laya	0.362	0.332	0.316	0.175	0.694
laya-multilingual	0.342	0.326	0.439	0.285	0.687
Jev 1.13.0 (published)	0.727	0.580	0.148	0.144	0.391
teacher self-agreement ceiling	0.735
per-question majority class	0.461
random guess	0.318

The fine-tuned checkpoint beats Jev by 3.9 points and clears the teacher ceiling, with 2.4x better Brier and 1.6x better score MAE. It wins on all four workflows: invoice processing 0.804, security incidents 0.766, customer service 0.764, agent-trace observability 0.730. By primitive: noul 0.857, choice 0.733, score 0.723.

Two places it still trails Jev: soft accuracy (0.471 vs 0.580 — its argmax is better but its distributions match the teacher less well) and ECE (0.213 vs 0.144), which temperature fitting addresses.

The base checkpoints sit below the majority-class baseline (0.362 and 0.342 against 0.461). All of the capability on this benchmark comes from fine-tuning.

Multilingual (51 languages, MASSIVE intent, 20 options, random = 0.050)

	laya	laya-multilingual
English	0.783	0.657
13 other languages	0.306	0.451
XNLI, English	0.860	0.843
XNLI, 14 other languages	0.521	0.731

Across all 51 languages the English checkpoint macro-averages 0.227 with macro ECE 0.733, and only 23 of 51 languages clear 3x random. Khmer scores 0.000 at 95.2% confidence. This is why Router exists: the model's own confidence gives no warning, so the routing decision has to be made before the forward pass.

English tasks

task	laya	laya-multilingual	note
AG News	0.947	0.937	in training mix
BoolQ	0.830	0.787	in training mix
DAIR Emotion	0.573	0.513	held out
prompt-injections	0.698	0.578	held out, n=116
SST-5 (ordinal)	0.372	0.282	held out

Calibration

Both checkpoints are over-confident as shipped. Refitting one temperature per (question type, option count) on held-out data moves mean ECE 0.466 -> 0.081 (laya) and 0.314 -> 0.106 (laya-multilingual). laya-multilingual ships with no fitted temperatures at all, so fit them before relying on its probabilities.

Honest limits

• The base checkpoints are near chance on typed-decisions zero-shot -- 0.362 and 0.352 against a 0.318 random baseline and a 0.461 majority-class baseline. The 0.766 figure comes from the checkpoint fine-tuned on that benchmark's own training split. Laya is a fast base to specialise, not a zero-shot decision engine.
• Keep choice questions under ~20 options. Every option is rendered into a fixed head_max_len budget (192 tokens on laya, 256 on the others), so a 77-option question leaves roughly 4 tokens per label and the option text stops being distinguishable — accuracy falls off sharply. Split large label spaces into a coarse choice followed by a fine one.
• Ordinal score questions are the weakest primitive (SST-5 0.372).
• laya collapses outside English; laya-multilingual is weaker on English. Route, or pick deliberately.

---

Live Demo & Resources

• Hugging Face Model: convaiinnovations/laya
• Interactive Web Demo: convaiinnovations/laya-demo
• Engineering Writeup: Read the full story on Dev.to

---

Fine-Tuning

Fine-tune Laya on your own domain data. The notebook runs on Kaggle's free 2xT4 GPUs and does the whole loop: build the dataset, train with RLCD (proper-scoring-rule rewards, GRPO-style policy gradient), fit calibration temperatures, evaluate, and push the result to the Hub.

• notebooks/laya_finetune_typed_decisions_2xT4_kaggle.ipynb

Fine-tuning is where most of the value is. On the typed-decisions benchmark the base checkpoints score near chance zero-shot (0.36 and 0.35 against a 0.318 random baseline), while the fine-tuned checkpoint reaches 0.766 on the same 2,000 decisions -- above TypeSafe Jev's published 0.727 and above the 0.735 teacher self-agreement ceiling. Treat Laya as a fast base to specialise, not as a zero-shot decision engine.

Runtime on 2xT4 is roughly 4-5 hours for 4 epochs over ~30k questions.

---

Support the Project

If Laya helps your research or products, consider supporting independent research:

---

License

Apache 2.0. Developed by Convai Innovations.