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: 1,308 Bytes
7c85c7e | 1 2 3 4 5 6 7 8 9 10 | # Model repository update — 2026-09-24
This upload replaces the short model card with a full model overview, measured public accuracy, CPU/GPU timing, usage methods and reproducibility links. It adds inference examples, a serving guide, a standalone public JevBench v1.4.1 runner, a CPU-specific entry point, a CPU notebook, two focused charts, a combined chart, and the exact results in JSON.
The `config.json` serving option limit changes from **64 to 128** tokens to align the default API with the measured evaluation input limit. The checkpoint and runtime source files are not modified. `manifest.json` is regenerated for every changed or new repository file; existing weight and runtime hashes are preserved.
The reported public accuracy is **107 / 231 = 46.32%** at the pinned model revision `14bf8c26bf344ebdf88e22a4b6152dc5f75f3578`. The official JevBench composite score is unavailable because the sealed/judge portion and cost input are not included. Local CPU and GPU speeds are measured at distinct batch sizes and are identified as such.
Upload the contents of this package to the **root** of `Q1z/Pivot`, replacing the names it contains. No delete operation is required. Model weight files, tokenizer files, and pre-existing serving request/response examples stay in the Hub repository.
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