Laya

Fast, non-autoregressive System 1 decision engine with mathematically calibrated probabilities.

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:

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.

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):

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).

Memory

All three together are ~1.16B parameters, so Router keeps one resident by default and evicts least-recently-used:

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

Decision Primitives

Benchmarks

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)

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.

Against Jev, on identical public datasets

Jev numbers are published by third parties, not measured here (no TypeSafe API access). Sample sizes and prompts differ, so read these as indicative rather than a controlled head-to-head.

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

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

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

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.
• 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.