Integrating Laya

Laya answers typed questions about a piece of text. You give it a state (the text) and a dict of questions; it returns a probability for every option of every question in one forward pass. It never generates text, so it cannot answer off-schema, and it is small enough (322M-421M parameters) to run inside the user's own process.

Reach for it when the project needs a decision, not a sentence: which queue, is this spam, how urgent, should the agent stop. Do not reach for it when the task needs reasoning over several steps, arithmetic, extraction of free-form values, or any generated text. For those, keep the LLM and use Laya in front of it as a cheap first pass (see "Cascade" below).

Install and first call

pip install laya        # pulls torch, transformers, safetensors, huggingface_hub

import laya

agent = laya.load("convaiinnovations/laya")          # English checkpoint

result = agent.predict(
    {"subject": "Charged twice", "body": "Refund the duplicate today or we cancel."},
    {
        "department": {
            "type": "choice",
            "instructions": "Which department should handle this request?",
            "criteria": {
                "billing": "invoices, payments, refunds",
                "technical": "bugs, outages, system errors",
                "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 leave?"},
    },
)
answers = result["answers"]

state may be a string, a dict or a list; dicts and lists are serialised to JSON. Load the model once at startup and reuse it. Loading takes 25-35 s; a call takes milliseconds.

The three question types

Every answer also carries action.act_probability from the model's act-or-escalate head, and result["usage"]["input_tokens"] reports the tokens consumed. confidence is one minus the normalised entropy of the distribution, so it is low whenever probability is spread out, even if the top option is right.

All questions in one predict call share a single forward pass, so ask everything you need about a state in one call rather than looping.

Writing questions that work

This matters more than anything else in the integration. Laya is an encoder doing something close to textual entailment, and it rewards questions shaped like that.

• Ask what the text says, not what to do about it. "Where is the bird relative to the gap?" produced clean graded probabilities; "Which way must the bird move?" came out inverted on every checkpoint, because the option word "up" is pulled towards "above" in the state. Ask a perception question, then let your code map the answer to an action.
• Put the state into words, never numbers. Given "Bird altitude: 20. Gap altitude: 60." no checkpoint could tell which was lower. If a decision depends on a comparison, threshold or sum, compute it in code and hand Laya the conclusion ("the bird is far below the gap").
• Describe each option. {"billing": "invoices, payments, refunds"} beats a bare ["billing", ...], because the description is what the model matches the text against.
• Keep option lists short. Each option is truncated to 48 tokens and all options for a question share a budget (192 tokens on the English checkpoint, 256 on the others). Upstream reports accuracy degrading past roughly 20 options. If a question raises ValueError: ... options exceed head_max_len, shorten the descriptions or split the question.
• Keep the state short and front-loaded. The English checkpoint reads 512 tokens in total, the others 1024, and an over-long state is cut from the end. Put what matters first. For emails, laya.email_state(subject, body, sender=...) strips quoted replies and signatures.
• Try two or three phrasings and measure. Small wording changes move results a lot. One concrete case: "blocked by a barrier" separated lanes far better than "blocked by a train".

Ready-made question sets exist for common jobs: laya.triage_questions(), laya.email_questions(), laya.guard_questions(), laya.moderation_questions(), laya.router_questions(). Use them as starting points and read what they contain before relying on them.

Choosing a checkpoint

agent    = laya.load("convaiinnovations/laya")                              # English, 421M
agent_ml = laya.load("convaiinnovations/laya", subfolder="multilingual")    # 100+ languages, 322M, ~1.6x faster
agent_td = laya.load("convaiinnovations/laya", subfolder="typed-decisions") # fine-tuned on four upstream workflows

Use the English checkpoint for English text. It is the best general performer and its probabilities are temperature-calibrated. Use multilingual for anything else. It ships uncalibrated (temperature 1.0), so expect it to report 100% and 0% readily; do not read those as certainty. It also missed explicit cancellation threats that the English checkpoint caught. typed-decisions only helps if your questions resemble the workflows it was tuned on.

laya.Router picks a checkpoint per request from the script and language of the state:

from laya import Router
router = Router(preload=True)                       # all three resident, ~4.6 GB in fp32
res = router.predict(state, questions)              # res["routing"] says which model and why
res = router.predict(state, questions, lang="pl")   # force the language when you know it

Pass lang= whenever the application knows the language. As of laya 0.3.4 the detector only recognises English, French, German, Spanish, Portuguese, Italian and Dutch among Latin-script languages. Polish, Czech, Turkish, Swedish and others are silently sent to the English checkpoint, which then answers confidently and wrongly. Non-Latin scripts route correctly.

Acting on probabilities

The probabilities are the product. Decide per question what happens at each confidence level, and make the threshold reflect the cost of being wrong:

a = answers["department"]
if a["probabilities"][a["choice"]] >= 0.85:
    route(a["choice"])
else:
    send_to_review(a)            # a person, a slower model, or a queue

For yes/no gates compare answers[id]["noul"] against a threshold chosen on real data, not 0.5 by default. For a guardrail, where a miss is expensive, set it low; for an auto-action, where a false alarm is expensive, set it high.

Cascade with an LLM. A good default architecture: Laya handles every request, and only the ones below the threshold go to the LLM or a person. The share that escalates is what you pay LLM latency and cost on, so measure it.

Calibrate on the user's own data before trusting the numbers. Temperatures live on the agent: agent.temperature is [choice, score, noul] and agent.temperature_by_options holds per-option-count overrides (keys like "choice:3-5", "noul:2") that take precedence. To refit: set agent.temperature = [1.0, 1.0, 1.0] and agent.temperature_by_options = {} to read raw probabilities p, fit a scalar T per question type that minimises negative log-likelihood of p ** (1 / T) (renormalised) on a labelled set, then write the fitted values back. A few hundred labelled examples are enough to see whether it helps.

Wiring it into a project

• Python service: build one agent (or Router) at startup and share it. Guard predict with a lock or a single worker queue; one GPU serves one forward pass at a time, and concurrent calls from several threads only interleave badly.

• Anything else (Node, Go, a front end): run Laya as a small local HTTP sidecar and call it over loopback. Bind to 127.0.0.1, enable keep-alive, and return the predict result as JSON. A minimal version:

  import threading, laya
  from fastapi import FastAPI
  app, lock = FastAPI(), threading.Lock()
  agent = laya.load("convaiinnovations/laya")
  
  @app.post("/predict")
  def predict(body: dict):
      with lock:
          return agent.predict(body["state"], body["questions"])

• Device: Laya picks CUDA, then Apple MPS, then CPU, and falls back to CPU if the GPU runs out of memory. Pass device="cpu" to force it. Measured for one question: about 34 ms (English) and 21 ms (multilingual) on an M1 Max GPU, 139 ms and 58 ms on its CPU. Ten questions in one call cost about 7-16 ms each.

• Warm up. The first call at a new batch shape compiles kernels and can take several times longer. Make one throwaway predict call at startup with a representative question set.

• Downloads. Weights come from the Hugging Face Hub on first load. The root repo bundles all three checkpoints, and loading the English one without a subfolder fetches the whole bundle (about 2.3 GB); a subfolder= load fetches only that checkpoint (650-850 MB). If a download hangs at 0 bytes, set HF_HUB_DISABLE_XET=1 to fall back to plain HTTPS. Once cached, set HF_HUB_OFFLINE=1 to skip network checks. Set USE_TF=0 if transformers stalls on import.

Evaluate before shipping

Zero-shot quality varies a lot by task. Upstream's own notes report the base checkpoints near chance on their typed-decisions benchmark without fine-tuning, ordinal score questions as the weakest type, and moderation not holding up on held-out data.

An independent run on 500 labelled examples (English checkpoint, no tuning, September 2026) shows where the line falls. Simple classification is strong: 93% on news topic (a dataset in Laya's training mix) and 96% on SMS spam, level with a hosted commercial model. Subtler or graded questions are not: 45% on six-way emotion, 65% on prompt-injection detection and 35% on a five-level star rating, where the hosted model scored 53%, 71% and 70%. Expected calibration error was 0.05-0.06 on the easy tasks and 0.34-0.40 on the hard ones. Latency was 35-66 ms per question. So expect Laya to work out of the box for clear-cut categories and yes/no checks, and plan to rephrase, fine-tune or cascade for ordinal scores and nuanced judgements. Measure:

1. Collect 50-200 real examples with the correct answers.
2. Run them through predict and record accuracy per question, plus how often the top probability clears your threshold and how often it is right when it does.
3. Try alternative phrasings and checkpoints on the same set, and keep the best.
4. If accuracy is still short, fine-tune. The upstream repository (github.com/NandhaKishorM/laya) ships a fine-tuning notebook that runs on free Kaggle GPUs.

Tell the user the measured numbers and the escalation rate, not just that it works.

Checklist

• Model loaded once, warmed up, and guarded by a lock or queue
• Questions ask what the text says; numbers and comparisons resolved in code first
• Every option has a description; option lists are short
• lang= passed wherever the language is known
• Thresholds chosen from labelled data, with a path for low-confidence cases
• Accuracy and escalation rate measured on the user's own examples and reported to them

Skill by brain function collapse (https://brainfunctioncollapse.com/laya). Laya itself is created by Nandakishor M, Convai Innovations, and released under Apache-2.0 (https://github.com/NandhaKishorM/laya).