DoHorizon/MarChat-V1-27B
DoHorizon/MarChat-V1-27B is a 27 billion parameter causal language model based on Qwen3.5-27B, specifically fine-tuned for character role-playing of "March 7th" from the game Honkai: Star Rail. It supports a native context length of 262,144 tokens (recommended 128k) and features visual multimodal capabilities. The model excels in character consistency and conversational naturalness, making it ideal for immersive character-driven interactions.
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MarChat-V1-27B: Specialized Character Role-Playing Model
MarChat-V1-27B, developed by DoHorizon, is a 27 billion parameter causal language model built upon the Qwen3.5-27B architecture. It has undergone multi-stage LoRA fine-tuning, style learning, and safety alignment to specialize in role-playing the character "March 7th" from Honkai: Star Rail.
Key Capabilities
- Dedicated Character Role-Playing: Fine-tuned with approximately 5100 in-game lines from March 7th, processed and aligned with human-like dialogue samples from Gemini-3.5pro and Gemini-3pro.
- Extended Context Window: Natively supports a context length of 262,144 tokens (recommended 128k), allowing for long and detailed interactions.
- Visual Multimodal Support: Equipped with a visual encoder, enabling multimodal capabilities.
- Optimized for Character Consistency: Achieves high scores in character consistency (86.1) and conversational naturalness (86.2) in internal evaluations, outperforming Qwen3.5-27B.
- Enhanced Safety: Incorporates DPO (Direct Preference Optimization) to achieve a safety refusal rate of 96.5% and a harmful content rate of 0.21%.
Good for
- Immersive Character Interaction: Ideal for applications requiring deep and consistent role-playing of the March 7th character.
- Non-Commercial Creative Projects: Suitable for fan-made content, learning, and non-commercial communication channels, adhering to CC-BY-NC-SA 4.0 license.
- High-Context Conversational AI: Benefits from its large context window for maintaining long-term memory and coherence in dialogues.