embeddings / lfm2.5-embedding-350m/hf-f35ae2c/bf16-liquidai/sentence-transformers-torch-mps-st5.1.1-tf4.56.2-torch2.14.1 /
2026-10-09-quick-card-prompts-249a86
By output dimension
| Dims | Score (95% interval over tasks) | Δ vs reference | Fidelity |
|---|---|---|---|
| 1024 | +0.02 [−0.03, +0.07] | losslesscos 0.9999 · top-10 0.98 |
Per task
MTEB main score ×100
| Task | 1024 |
|---|---|
| NanoArguAnaRetrieval | 50.40 |
| NanoClimateFeverRetrieval | 45.07 |
| NanoDBPediaRetrieval | 65.01 |
| NanoFEVERRetrieval | 95.31 |
| NanoFiQA2018Retrieval | 55.73 |
| NanoHotpotQARetrieval | 85.23 |
| NanoMSMARCORetrieval | 64.11 |
| NanoNFCorpusRetrieval | 36.93 |
| NanoNQRetrieval | 72.41 |
| NanoQuoraRetrieval | 89.56 |
| NanoSCIDOCSRetrieval | 39.12 |
| NanoSciFactRetrieval | 74.99 |
| NanoTouche2020Retrieval | 54.21 |
| STSBenchmark | 80.11 |
| Banking77Classification | 82.91 |
Throughput
- Prefill tokens/s (engine)
- 6,203 (embedding has no decode)
- Prefill tokens/s (wall)
- 6,181 (includes HTTP and base64)
- Texts
- 66,939 texts · 9,883,483 tokens · 41.9 texts/s
- Engine
- backend=sentence-transformers · device=mps · dtype=bfloat16 · torch=2.14.1 · cuda_arch_list=null · device_name=mps · sentence_transformers=5.1.1 · transformers=4.56.2 · max_seq_length=512 · dimension=1024 · trust_remote_code=true · unpadded=true · triton_overrides=null · 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
- 2026-10-09 00:36:51 UTC · 28 min
- Hardware
- Apple M4 Pro · 24 GB
- Harness
- bench @ 2298f0b · scorer mteb 2.22.5
- Suite hash
- 4c216f567e7755fcba7cd61c6ffe3e6ea2c399c52c3d2a2a6aedefb20066cf05
- Recipe
- lfm2.5-embedding-350m/apple-m/st · profile st-bf16 · inference-engines @ 1ef8974
- Latency
- p50 593 ms · p95 1.89 s · p99 1.94 s (per request)
- Usage
- 9,883,483 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 lfm2.5-embedding-350m/apple-m/st \
--recipe-profile st-bf16 \
--on <host with a apple-m:m4-pro> \
--suite embeddings --tier quick --profile card-prompts \
--reference results/lfm2.5-embedding-350m/hf-f35ae2c/fp32-liquidai/sentence-transformers-torch-cuda-st5.1.1-tf4.56.2-torch2.14.1-cu126/embeddings/2026-10-08-quick-card-prompts-efe68aFiles
exactly as committed under results/