d1:3b
d1 · LiquidAI · 3B · llama.cpp · LFM Open License v1.0
- Score
- 63.1
- 95 % interval 61.4–65.0
- Rank
- 2
- of 35 scored models
- Public benchmark
- 75.1
- macro accuracy, %
- typed-decisions
- 65.0
- 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 | 75.1(73.7–76.3) | 79.0 | 87.5 | 47.6 | 0.063 | 0.334 | 0 / 3,880 |
| typed-decisions | 65.0(62.8–67.3) | 60.3 | 80.2 | 57.1 | 0.035 | 0.461 | 0 / 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 | 50.8 ms | 52.5 ms | 6.86 s | 4.0 GB | 3.1 GB | Q8_0 |
| Threadripper 3960X CPU | 1.76 s | 1.94 s | 6.61 s | – | 3.3 GB | Q8_0 |
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 d1:3b
# 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 d1:3b \
--url http://127.0.0.1:11435 --data typed.jsonl --out typed/d1-3b
# Bespoke Labs' public benchmark; build its 13 subsets first
NIMBLE_SRC=nimble python -m ollaya_convert.bench_public run --model d1:3b \
--url http://127.0.0.1:11435 --data public --out public/d1-3b
# speed and memory
python -m ollaya_convert.bench_latency --models d1:3b --memory \
--machine my-machine --device cuda --out latency.json