layarejects 8 %router
Laya · Convai Innovations · · router · Apache-2.0
- Score
- 30.4
- 95 % interval 28.4–32.4
- Rank
- 25
- of 35 scored models
- Public benchmark
- 53.5
- macro accuracy, %
- typed-decisions
- 35.8
- accuracy, %
By task
Chance-corrected accuracy on each of the 14 tasks: 0 is guessing, 100 is every answer right.
Accuracy and calibration
| Benchmark | Accuracy | Choice | Noul | Score | ECE | Brier | Rejected |
|---|---|---|---|---|---|---|---|
| Public benchmark | 53.5(52.2–54.7) | 65.8 | 68.0 | 8.7 | 0.103 | 0.476 | 452 / 3,880 |
| typed-decisions | 35.8(33.3–38.1) | 28.3 | 47.8 | 32.3 | 0.174 | 0.750 | 7 / 400 requests |
Accuracy in %, with its 95 % bootstrap interval. A rejected request (the model can’t take the question, or the input is too long for it) counts as wrong.
Speed and memory
| Device | p50 | p90 | Load | VRAM | RAM peak | Precision |
|---|---|---|---|---|---|---|
| RTX 5090 CUDA | – | – | – | – | – | – |
| Threadripper 3960X CPU | – | – | – | – | – | – |
Five-question requests over HTTP, one at a time, each a different short message. VRAM: the rise of the GPU’s used memory while the model is loaded. RAM peak: the runner process’s peak resident memory.
Parity with the authors’ code
Its parity check is in the model’s family documentation in the repository (docs/families/); the compiled parity file does not list it yet.
Reproduce
The tools are in convert/ollaya_convert and need only Python’s standard library. Run them against a local Ollaya 0.13.0 with the same model digest.
ollaya pull laya
# typed-decisions; export the test split once with
# uv run python -m ollaya_convert.bench_typed export --out typed.jsonl
python -m ollaya_convert.bench_typed run --model laya \
--url http://127.0.0.1:11435 --data typed.jsonl --out typed/laya
# Bespoke Labs' public benchmark; build its 13 subsets first
NIMBLE_SRC=nimble python -m ollaya_convert.bench_public run --model laya \
--url http://127.0.0.1:11435 --data public --out public/laya
# speed and memory
python -m ollaya_convert.bench_latency --models laya --memory \
--machine my-machine --device cuda --out latency.json