Instructions to use litert-community/Laya-Multilingual-LiteRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use litert-community/Laya-Multilingual-LiteRT with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Laya
How to use litert-community/Laya-Multilingual-LiteRT with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 4,648 Bytes
eae8f62 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | # Laya validation data
Large model files and captured fixtures are external to the module. Building an APK does not
need them. Host parity tests require a local validation bundle specified by `LAYA_TEST_DATA`;
they must not resolve data from another checkout or download it during a test.
```text
validation-data/
host_assets/
tokenizer.json
laya_ml_calibration.json
token_embeddings_fp16.bin
token_embeddings.json
fixtures/
ml_fixtures.json
ml_rows_s256.json
ml_rows_s512.json
tokenizer_stress.json
calibrated_reference.json
serialization_reference.json
fp16_conversion_reference.bin
embedding_lookup_reference.json
embedding_lookup_reference.bin
gate_rows_s256.json # optional: debug device gate
```
Run from the module root after supplying this bundle:
```bash
export LAYA_TEST_DATA="$PWD/validation-data"
./gradlew --no-daemon :app:testDebugUnitTest
```
`LAYA_TEST_RESULTS` optionally selects a separate report directory. A missing or incomplete
bundle must not be counted as a passing parity gate. The model graphs are not used by JVM
host-parity tests.
## Captures and checks
The source checkpoint is `convaiinnovations/laya`, revision
`1c5edc17a7acd8701df6fc341c0d179f1c62c982`, multilingual branch. Captures came from the official
laya 0.3.4 builder and decoder. Each captured row records its fixture/question identity, window,
ordered question schema, unpadded sequence IDs, marker positions, question type, option count,
raw marker logits, raw action logits, and official answer dictionary at T=1.
| Input | Content | Required assertion |
|---|---|---|
| `ml_fixtures.json` | 44 synthetic EN/JA states and schemas | State/schema source for rebuilding captured rows |
| `ml_rows_s256.json`, `ml_rows_s512.json` | 201 question rows per window | 402/402 exact sequence IDs and marker positions |
| Same captured rows | Raw logits and action logits | 402/402 exact official four-decimal dictionaries at T=1 |
| `calibrated_reference.json` | NumPy host decode of the same tensors with shipped temperatures | 402/402 exact calibrated dictionaries |
| `tokenizer_stress.json` | 300 synthetic strings encoded with tokenizers 0.23.2 | 300/300 exact token ID lists |
| `serialization_reference.json` | 20 nested/string/Unicode/boolean/null states and Python `json.dumps` outputs | 20/20 exact strings |
| `embedding_lookup_reference.json` and `.bin` | NumPy FP16-table lookup converted to little-endian float32 for 402 padded rows | 118,554,624 values bit-exact, maximum absolute error 0 |
| `fp16_conversion_reference.bin` | All 65,536 half-float bit patterns converted with NumPy | Float32 bits match, including infinities and NaN payload behavior |
The stress corpus covers Japanese, English, mixed scripts, emoji, URLs, digits/units, repeated
whitespace, boundary whitespace, added tokens, accents, Korean, Chinese, Arabic, and empty text.
The embedding reference binary is **474,218,496 bytes**; its manifest pins the source table and
row IDs. Padded positions must gather token ID 0. Fixture generation uses the pinned tokenizer,
Python serialization, and NumPy host math; generating these references does not execute a model.
## Debug device rows
`gate_rows_s256.json` joins the 201 S256 captures to their source states and question schemas.
It retains IDs, markers, question type, option count, raw captures, and official T=1 answers so
the Android runner can tokenize and build each row independently before model inference.
The current file is **1,018,117 bytes**, SHA-256
`037bd3c83f02f5aade4c559984a55d31cd4c231af493aee5fb880a4af35a7aa8`.
The module does not bundle the gate file or raw calibration corpora. Supply the independently
prepared file with `scripts/install_to_device.sh --gate-rows FILE` alongside the model assets.
The installer places it in the app's private `files/fixtures/` directory. The `gate=true` intent
entry exists only in the debug source set; release builds launch the normal product UI.
Gate acceptance compares all 201 token sequences and marker lists exactly, all 81 choice/score
argmax results under the recorded tie rule, every option/noul/action probability within 0.01 of
the captured official dictionary, and finite outputs. GPU acceptance additionally requires
explicit FP32 precision and the runtime's full-residency, one-partition delegate evidence.
Per-row reports contain creation, tokenizer, embedding lookup, and write/run/read timings;
the first graph call is separate from the remaining 200 warm rows. Matching these captures
checks conversion and host parity, not classification accuracy on a held-out dataset.
|