license: apache-2.0
library_name: coreml
pipeline_tag: text-classification
base_model: convaiinnovations/laya
tags:
- coreml
- laya
- apple-silicon
- neural-engine
- decision-model
- fluidaudio
laya-coreml
Core ML conversion of laya-multilingual (Convai Innovations, Apache-2.0): a 322M-parameter
mmBERT-base encoder with a typed decision head that answers choice, score, and noul
questions about a text state in one forward pass, returning calibrated probabilities and no
generated tokens. Weights are unchanged from
convaiinnovations/laya multilingual/ at
revision 1c5edc17a7acd8701df6fc341c0d179f1c62c982.
Runs through FluidUse (LayaManager) on macOS 14+.
let laya = try await LayaManager.load() // downloads the 128 + 512 buckets and tokenizer.json
let answer = try await laya.answer(
state: "The T piece dropped at column 3 leaves one hole under it.",
question: .noul("Is this a clean placement?"))
print(answer.noul!) // P(true)
swift run -c release FluidUseLaya answer --state "…" --type choice \
--instructions "What does the customer want?" --options "refund|order status|technical help"
swift run -c release FluidUseLaya tetris # headless Tetris played by laya decisions
swift run -c release LayaTetrisDemo # SwiftUI demo
Files
| File | Tokens | Notes |
|---|---|---|
laya_multilingual_fp16_L128_options32.mlmodelc |
128 | Short prompts; runs on CPU + Neural Engine |
laya_multilingual_fp16_L256_options32.mlmodelc |
256 | |
laya_multilingual_fp16_L512_options32.mlmodelc |
512 | Long states; GPU is faster than ANE here |
laya_multilingual_fp16_L1024_options32.mlmodelc |
1024 | Upstream max_len; GPU |
tokenizer.json |
mmBERT / Gemma vocabulary (256k), byte fallback |
Each bucket is a complete FP16 model (614 MB, 393 MB of which is the embedding table) with
32 option slots. FluidUse picks the smallest loaded bucket that fits a prompt and truncates
the state on the right for the largest one, exactly like laya's max_len.
Inputs: input_ids int32 [1, L], attention_mask int32 [1, L], marker_map float32
[1, 32, L] (one-hot [MASK] position per option), question_type float32 [1, 3].
Outputs: logits [1, 32], probabilities [1, 32], action_probabilities [1, 2].
Sequence format: [CLS] <type> question: <instructions> [SEP] ([MASK] <option>)* [SEP] <state> [SEP].
Parity and latency
Apple M5 Pro, macOS 27.0, 16 fixture questions vs. the unmodified PyTorch FP32 runtime:
16/16 argmax agreement on every bucket and compute-unit setting, max probability error 0.0021
(ALL) / 0.0126 (CPU_AND_NE). Per-question latency, warm:
| Bucket | CPU + ANE | All units |
|---|---|---|
| L128 | 3.6 ms | 3.9 ms |
| L256 | 9.9 ms | 5.2 ms |
| L512 | 27.5 ms | 9.0 ms |
| L1024 | 80.1 ms | 17.9 ms |
On laya's published application suites (3,899 questions, seed 13, rebuilt from upstream's scripts), the Core ML buckets answered from Swift match the PyTorch reference's accuracy on every suite at 5.2 ms median per question (p95 18 ms):
| Suite | Upstream (T4, PyTorch) | Core ML (M5 Pro) |
|---|---|---|
| jev.ag_news | 0.930 | 0.935 |
| jev.emotion | 0.530 | 0.537 |
| massive_intent.en | 0.657 | 0.657 |
| app.support_triage | 0.522 | 0.542 |
| app.email_spam | 0.993 | 0.993 |
| app.phishing | 0.993 | 0.993 |
| app.guardrails_jailbreak | 0.755 | 0.805 |
| app.moderation_toxicity | 0.525 | 0.535 |
| app.rag_relevance | 0.657 | 0.672 |
| app.model_routing_domain | 0.123 | 0.441 |
Conversion pipeline, verification reports, and Swift parity fixtures:
mobius models/computer-use/laya/coreml.
License
Apache-2.0, following the upstream weights and code by Convai Innovations (NandhaKishorM/laya). Independent conversion; not an official Convai Innovations release.