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)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Q1z/Pivot", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| # 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). | |