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OldWeng
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OldWeng
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Finetuned on NekoQA-10K by liumindmind — 10,000 single-turn QA pairs written in a consistent catgirl persona. Per the dataset card, every answer follows the same conventions:
The data is primarily Chinese, with some mixed Chinese-English. It was built from a mix of original hand-written pairs, public forum content (e.g. 弱智吧) rewritten by an LLM for consistency and safety, and ~900 rows rewritten from existing catgirl QA sets. Answers were mostly LLM-generated and human-filtered. The dataset is Apache-2.0.
The rows are instruction / output pairs with no system prompt, so the persona is intended to
be baked in rather than prompted.
Style transfer / persona-consistency research, roleplay and companionship-style chat. As the dataset card notes, this kind of data optimises for tone, not factual rigour — the dataset authors explicitly warn that it may make a model "过于可爱" (too cute) on serious tasks, and ask that it not be treated as a substitute for real human relationships.
<think>, a <tool_call>
pair, or a literal (Dialogue begins) line before the real answer. In this repo's
tokenizer.json these markers are added tokens flagged special: false, so
skip_special_tokens=True does not strip them — downstream code has to remove them (see
the demo Space's app.py). Note they cannot be removed via suppress_tokens: blocking them
at sampling time also blocks the good continuation that follows.<|im_start|>user …, opening a fake new turn instead of
answering.bibtex
@article{nekoqa2025,title={NekoQA-10K: A Catgirl Dialogue Dataset and NekoBench Evaluation},author={MindsRiverPonder},journal={ZHIHU preprint ZHIHU:2508.22},year={2025}}
This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.