Feature Extraction
Transformers
Safetensors
pivot
decision-making
classification
scoring
custom_code
Instructions to use Q1z/Pivot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Q1z/Pivot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Q1z/Pivot", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Q1z/Pivot", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,866 Bytes
7c85c7e | 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 | # Pivot performance
All figures here refer to checkpoint `14bf8c26bf344ebdf88e22a4b6152dc5f75f3578`, public JevBench v1.4.1 commit `24b9b5c1609a7a9e8fa14f49e5985a836c9dc842`, FP32 and the same frozen 512/128-token input contract.
## Accuracy on public tasks
| Tier | Correct | Tasks | Accuracy | Top-label ECE, 10 bins |
|---|---:|---:|---:|---:|
| Original | 27 | 72 | 37.50% | 0.5247 |
| Easy | 39 | 48 | 81.25% | 0.1139 |
| Hard | 41 | 111 | 36.94% | 0.3792 |
| **Total** | **107** | **231** | **46.32%** | — |

The official JevBench composite score is **unavailable** because the sealed and judge tasks and official cost input were not measured.
## Local speed
| Warm local FP32 measure | H200 GPU | Xeon CPU, 4 threads |
|---|---:|---:|
| Single decision p50, 32 measured | 15.7668 ms | 797.5558 ms |
| Single decision p95, 32 measured | 19.8708 ms | 1089.0266 ms |
| Single decision mean | 16.1614 ms | 770.7159 ms |
| Batch size for throughput | 32 | 4 |
| Throughput, median of 3 × 64 decisions | 545.2833 decisions/s | 3.7708 decisions/s |

The CPU p50 single-decision time is **50.6×** the H200 p50 for these two machines. CPU and GPU batch throughput used different batch sizes and should not be read as a same-batch comparison. The timings cover tokenizer + inference + scoring with 5 warmup singles and 2 warmup bulk passes. Reproduce them on your own hardware using the [CPU script](../cpu-speed/README.md) or [full public runner](../benchmarks/README.md).
Source: [full structured summary](../evaluation/2026-09-24/performance.json), [original public benchmark result](../evaluation/2026-09-24/jevbench_public.json), and [original CPU measurement](../evaluation/2026-09-24/cpu_speed.json).
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