convaiinnovations/laya · Hugging Face

Source: https://huggingface.co/convaiinnovations/laya Laya is an open-source, non-autoregressive System 1 decision model: give it astate(text, email, ticket, or JSON document) andtyped questions, and it returns typed answers with mathematically calibrated probabilities and confidence scores. It never generates text, eliminating parsing errors and hallucinations.

This is thefine-tuned checkpoint, incorporating dedicated email triage (spam, phishing, department routing), conversation trajectory modeling (TD(lambda = 1.0)), and per-cardinality temperature calibration.

Question typeReturnschoiceSelected option, probabilities per option, calibrated confidencescoreExpected level on your ordinal rubric (0, 1, 2...), distribution, confidencenoulCalibrated boolean probability P(true) from 0.0 to 1.0

https://huggingface.co/convaiinnovations/laya#architectureArchitecture

• Backbone:ModernBERT\-large(395M, fully fine-tuned, bidirectional), plus a decision head trained from scratch (2 transformer layers, an option marker scorer, and an act/escalate head). Total parameters: 421M.
• Option Markers: Every option is scored at its own\[MASK\]marker token, then a softmax is applied over that question’s options.
• Input budget: 512 tokens per question (question + options + state).
• Multi-question batching: Evaluates all questions in a single forward pass (~33 to 38 ms on GPU).

https://huggingface.co/convaiinnovations/laya#trainingTraining

Trained withRLCD (Reinforcement Learning for Calibrated Decisions): the policy reports a probability distribution, exploration adds zero-mean Gaussian noise to the logits, and the reward is a strictly proper scoring rule (log score + spherical score, plus ranked probability score for ordinal score questions). The maximum expected reward is achieved only when the model outputs true, calibrated probabilities.

Multi-turn dialogues use Temporal Difference learning with Monte Carlo targets (TD(lambda = 1.0)) over prefix slices, preventing outcome leakage. 100% human-annotated real-world datasets, zero synthetic shortcuts.

• Training: Fine-tuned (7,313 updates, 1 epoch, ~1.96 hours)
• Fitted calibration temperatures: [1.637, 1.251, 1.983] (with per-option-count scaling)

https://huggingface.co/convaiinnovations/laya#benchmark-laya-vs-typesafe-jevBenchmark: Laya vs. TypeSafe Jev

Metric / DimensionTypeSafe Jev (Published)Laya (Fine-Tuned Checkpoint)Analysis / AdvantageP50 Latency (1 Question)~400 ms avg (70 to 500 ms, 150 ms best)38.4 ms(p95: 42.1 ms)Laya is ~10.4x faster on avg (4x faster than Jev best-case)****Batched Latency (10 Questions)~1,500 ms (serial) / ~400 ms156.0 ms(p95: 158.4 ms)Laya evaluates 10 questions in the time Jev answers 1****Batched Latency (50 Questions)Multi-second / rate-limited721.4 msHigh-throughput parallel mini-batchingBenchmark Accuracy****67.8%(across 4 production workflows)83.8%in-task macro accuracyLaya achieves +16.0% higher overall accuracy****Intent & Customer Routing~95 to 98% agreement99.1% accuracy(ECE: 0.009)Near-zero calibration error on routingModeration & Content Safety~92 to 95% agreement96.7% accuracy(ECE: 0.061)Clean safety boundary separationInference & Fact VerificationNot separately reported88.3% accuracy(ECE: 0.054)Full bidirectional attention captures contradictionsInstruction-Following TasksProprietary internal set87.8% in-task / 86.3% zero-shotProven generalization across unseen tasksEmail Triage & PhishingVendor custom workflow73.2% accuracy(ECE: 0.017)Tailored email cleaning and phishing filtersSelective Automation (@ 50% Cov)Claims human escalation92.2% accuracy(ECE: 0.041)Safe automated gating (confidence >= 0.85)Model Weights & CodeClosed-source / proprietary API100% Open-source Apache 2.0Full data sovereignty and transparencyInference Cost0\.042 / 1M input tokens recurring**0.00 / self-hostedRuns on commodity GPUs, Mac MPS, or CPUMulti-Turn Trajectory ModelingStatic state snapshotsTD(lambda = 1.0) prefix modelingReal temporal credit assignmentDeployment ModeCloud-only egressAir-gapped / Local / On-Device**Zero data egress (HIPAA/GDPR compliant)

https://huggingface.co/convaiinnovations/laya#evaluation-results-this-checkpointEvaluation Results (This Checkpoint)

• **In-task test sets:**macro accuracy 0.838, macro ECE 0.060
• **Zero-shot (held-out task families):**macro accuracy 0.651, macro ECE 0.207
• Key Task Families:- Intent and routing: accuracy0.991, ECE 0.009 - Moderation and safety: accuracy0.967, ECE 0.061 - Emotion and tone: accuracy0.906, ECE 0.018 - Email triage and phishing: accuracy0.732, ECE 0.017 - Inference and fact checking: accuracy0.883, ECE 0.054

Detailed results by task family, reliability diagrams, and risk-coverage curves are located ineval/in this repository.

https://huggingface.co/convaiinnovations/laya#quickstart-pip-install-layaQuickstart (pip install laya)

import laya

# Load the model directly from Hugging Face Hub
agent = laya.load("convaiinnovations/laya")

state = {
    "from": "[email protected]",
    "subject": "Duplicate billing on March invoice #4411",
    "body": "Hi team, we were billed twice for March. Please refund the duplicate before Friday or we will cancel our plan."
}

questions = {
    "department": {
        "type": "choice",
        "instructions": "Which department should handle this email?",
        "criteria": {
            "billing": "invoices, payments, refunds",
            "technical": "bugs, outages, integrations",
            "sales": "pricing, contracts, demos",
            "other": "everything else"
        }
    },
    "urgency": {
        "type": "score",
        "instructions": "How urgent is this request?",
        "criteria": ["not urgent", "soon", "critical deadline or blocking issue"]
    },
    "churn_risk": {
        "type": "noul",
        "instructions": "Does the user threaten to cancel or switch to a competitor?"
    },
    "is_phishing": {
        "type": "noul",
        "instructions": "Is this email a phishing or scam attempt?"
    }
}

result = agent.predict(state, questions)
answers = result["answers"]

print("Department :", answers["department"]["choice"], f"(confidence: {answers['department']['confidence']:.2f})")
print("Urgency    :", f"{answers['urgency']['score']:.2f} / 2.0")
print("Churn Risk :", f"{answers['churn_risk']['noul']:.1%}")
print("Phishing   :", f"{answers['is_phishing']['noul']:.1%}")

https://huggingface.co/convaiinnovations/laya#interactive-web-demoInteractive Web Demo

Try the live Gradio Space:convaiinnovations/laya-demo

https://huggingface.co/convaiinnovations/laya#license-and-supportLicense and Support

Released under the Apache 2.0 License byConvai Innovations, who offer commercial support, enterprise integration, and custom fine-tuning.

https://huggingface.co/convaiinnovations/laya#limitationsLimitations

• Text only, English, 512 tokens per question (longer states are truncated).
• Calibration is measured on the benchmark datasets; evaluate on your own distribution before full automation.
• Arithmetic, counting, date comparisons, and multi-hop index lookups should be kept in deterministic code.