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
Download cpu-speed/README.md from Q1z/Pivot: direct link, hf CLI and curl.
- Browser
- Download file 1.14 kB
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https://huggingface.co/Q1z/Pivot/resolve/main/cpu-speed/README.md
- Command line
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hf download hf://Q1z/Pivot/cpu-speed/README.md
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curl -L -o README.md https://huggingface.co/Q1z/Pivot/resolve/main/cpu-speed/README.md
Pivot CPU speed
This folder is a CPU-only entry point to the standalone public JevBench runner. It downloads the exact measured Pivot checkpoint and the pinned official public JevBench source; no private account or token is needed for the public files.
From the model repository root on Windows PowerShell:
py -m pip install -r benchmarks/requirements.txt
py cpu-speed/benchmark.py --threads 4 --batch-size 4 --output pivot_cpu_results.json
The run measures warm FP32 latency for 32 individual decisions and throughput over three sets of 64 decisions. It includes tokenization, inference, and scoring. It reports p50, p95, mean, decisions per second, thread count, hardware, pinned revisions, and the contract. To evaluate public accuracy too, run py benchmarks/jevbench_public.py --device cpu --threads 4 --mode all.
The published 4-thread Xeon result is p50 797.6 ms, p95 1,089.0 ms, and 3.77 decisions/s at batch 4. Your CPU result will differ. GPU throughput used batch 32, so these throughput values are not a matched batch comparison. See archived measurements.