Instructions to use aac6fef/PasteWhat-Ranker-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use aac6fef/PasteWhat-Ranker-v1 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir PasteWhat-Ranker-v1 aac6fef/PasteWhat-Ranker-v1
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Initialize research model card with explicit pending release status
Browse files
README.md
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---
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license: apache-2.0
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base_model: convaiinnovations/laya-multilingual
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language:
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- en
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- zh
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tags:
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- clipboard
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- reranking
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- abstention
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- mlx
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- work-in-progress
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---
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# PasteWhat-Ranker-v1
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**Research in progress. No trained release or acceptance result is published yet.**
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Planned: decision-label distillation from `kimi-for-coding` into the non-quantized Laya-multilingual encoder, with candidate ranking and group-aware abstention heads. The model chooses existing clipboard content or returns null; it does not generate paste content.
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Training, independent synthetic evaluation, provenance and release status: [GitHub](https://github.com/mizorewww/pastewhat-ranker-v1).
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Model artifacts will be published with tokenizer, preprocessing, calibration, measured metrics, and limitations. Synthetic-only results will not be presented as real-user accuracy.
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