2026-10-07-quick-card-prompts-2fa048
By output dimension
| Dims | Score (95% interval over tasks) | Δ vs reference | Fidelity |
|---|---|---|---|
| 768 | −0.10 [−0.38, +0.09] | faithfulcos 0.9999 · top-10 0.98 | |
| 512 | +0.05 [−0.05, +0.18] | faithfulcos 0.9999 · top-10 0.98 | |
| 256 | +0.08 [−0.05, +0.22] | faithfulcos 0.9999 · top-10 0.97 | |
| 128 | +0.05 [−0.07, +0.19] | faithfulcos 1.0000 · top-10 0.96 |
Per task
MTEB main score ×100
| Task | 768 | 512 | 256 | 128 |
|---|---|---|---|---|
| NanoArguAnaRetrieval | 64.91 | 62.85 | 59.93 | 56.64 |
| NanoClimateFeverRetrieval | 27.97 | 28.56 | 27.62 | 26.75 |
| NanoDBPediaRetrieval | 65.17 | 64.24 | 63.78 | 57.72 |
| NanoFEVERRetrieval | 92.34 | 93.22 | 91.06 | 86.85 |
| NanoFiQA2018Retrieval | 44.75 | 44.02 | 42.76 | 33.42 |
| NanoHotpotQARetrieval | 89.56 | 88.29 | 87.53 | 78.56 |
| NanoMSMARCORetrieval | 55.50 | 55.13 | 54.78 | 50.24 |
| NanoNFCorpusRetrieval | 38.10 | 37.11 | 35.15 | 30.48 |
| NanoNQRetrieval | 78.49 | 76.70 | 73.28 | 71.47 |
| NanoQuoraRetrieval | 95.46 | 95.45 | 94.81 | 92.88 |
| NanoSCIDOCSRetrieval | 37.86 | 37.66 | 35.62 | 30.89 |
| NanoSciFactRetrieval | 88.08 | 89.61 | 87.24 | 77.83 |
| NanoTouche2020Retrieval | 44.07 | 44.58 | 42.86 | 38.01 |
| STSBenchmark | 87.48 | 87.54 | 87.37 | 86.90 |
| Banking77Classification | 92.28 | 92.26 | 92.07 | 91.27 |
Throughput
- Prefill tokens/s (engine)
- 2,756 (embedding has no decode)
- Prefill tokens/s (wall)
- 2,750 (includes HTTP and base64)
- Texts
- 66,939 texts · 11,589,994 tokens · 15.9 texts/s
- Engine
- backend=onnxruntime · onnx_file=model_quantized.onnx · requested_provider=CPUExecutionProvider · active_providers=CPUExecutionProvider · onnxruntime=1.30.0 · transformers=5.19.0 · max_seq_length=8192 · token_budget_per_forward=16384 · python=3.12.12 · platform=macOS-26.5.2-arm64-arm-64bit · machine=arm64 · numpy=2.5.3
Run
- Suite
- embeddings · quick tier · profile card-prompts · mode native
- Items
- 2,092 succeeded of 2,092 attempted (66,939 planned)
- When
- 2026-10-07 06:20:43 UTC · 1 h 14 min
- Hardware
- Apple M4 Pro · 24 GB
- Harness
- bench @ 5f5fc24 · scorer mteb 2.22.5
- Suite hash
- d499aab0d7091e3ba5b211e1cbaedf79ed4c9c86a7214156946d2ee4d21299ec
- Recipe
- embeddinggemma-2/apple-m/st-ort · profile ort-q8 · inference-engines @ 0ddc734
- Latency
- p50 969 ms · p95 6.34 s · p99 17.17 s (per request)
- Usage
- 11,589,994 input tokens · 0 output tokens · $0.00
- dimensions below 768 are the same vectors truncated and re-normalized (Matryoshka); fidelity compares each dimension with the reference at the same dimension
Reproduce
npm run bench -- run --recipe embeddinggemma-2/apple-m/st-ort \
--recipe-profile ort-q8 \
--on <host with a apple-m:m4-pro> \
--suite embeddings --tier quick --profile card-prompts \
--reference results/embeddinggemma-2/hf-914f7f8/fp32-google/sentence-transformers-torch-cuda-st6.1.0-torch2.14.1-cu126/embeddings/2026-10-07-quick-card-prompts-d0777fFiles
exactly as committed under results/