ThakiCloud/Qwen3.8-27B-Human-KO
ThakiCloud/Qwen3.8-27B-Human-KO is a 27 billion parameter Qwen3.8-based language model, fine-tuned for Korean conversation. It significantly shifts the output style from bulleted lists to short, flowing Korean prose, reducing bullet-list generation from 97.5% to 2.0%. This model also features CJK output suppression, blocking Chinese-word and kana tokens while preserving single-character Hanja for Korean glosses. It is optimized for human-like Korean conversational responses and maintains a 32768 token context length.
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Overview
ThakiCloud/Qwen3.8-27B-Human-KO is a 27 billion parameter model derived from Qwen3.8-27B, specifically adapted for Korean conversation. Its primary focus is on refining the output style and ensuring clean Korean generation by suppressing unwanted CJK characters.
Key Differentiators & Capabilities
- Korean Style Alignment: The model has been trained on an in-house synthetic corpus to produce short, flowing Korean prose, drastically reducing the rate of bullet-list generation from 97.5% to 2.0% and median response length from 1,326 to 220 characters.
- CJK Output Suppression: It actively blocks the generation of Chinese-word and kana tokens at the weight level, ensuring a cleaner Korean output. Single-character Hanja used for Korean glosses are preserved.
- Improved Human-likeness: Achieves a 94.9% win rate in pairwise human-likeness evaluations by an LLM judge, compared to the base model's 1.1%.
- Code Performance: Shows a slight improvement in HumanEval scores (+3.03 percentage points).
Important Considerations
- Output Tendency: The model defaults to short answers. For longer, structured documents, explicit length and format instructions are necessary in the prompt.
- Unmeasured Axes: English and mathematical performance have not been extensively measured with sufficient samples.
- Safety Alignment: Inherits the safety alignment of the base Qwen3.8-27B model without separate safety training.
- Hanja Reading: While it can read Hanja, it cannot generate Chinese-word tokens.
Research
The style alignment behind this model is further detailed in the paper "Off-Target Effects of Response-Style Alignment in a Korean 27B Language Model" (arXiv:2609.11291).