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
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Download docs/UPDATE_NOTES.md from Q1z/Pivot: direct link, hf CLI and curl.
- Browser
- Download file 1.31 kB
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https://huggingface.co/Q1z/Pivot/resolve/main/docs/UPDATE_NOTES.md
- Command line
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hf download hf://Q1z/Pivot/docs/UPDATE_NOTES.md
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curl -L -o UPDATE_NOTES.md https://huggingface.co/Q1z/Pivot/resolve/main/docs/UPDATE_NOTES.md
1.31 kB
| # 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. | |