Kezmark/Mordant-12B-Think
Kezmark/Mordant-12B-Think is a 12 billion parameter, Mistral-Nemo-2407-based language model fine-tuned for AI image generation composition. It excels at producing detailed, narratively rich compositions with chain-of-thought reasoning, frequently self-critiquing decisions and explaining compositional choices. This model is optimized for generating spatially structured image prompts and complex multi-element scenes requiring narrative-level spatial reasoning.
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Mordant-12B-Think: Flagship Compositional AI
Kezmark/Mordant-12B-Think is the flagship model of the Mordant family, a 12 billion parameter model based on Mistral-Nemo-2407. It is specifically fine-tuned for AI image generation composition, focusing on creating detailed and narratively rich prompts.
Key Capabilities
- Advanced Chain-of-Thought Reasoning: The model demonstrates genuine analytical depth, self-critiquing decisions and explaining why specific elements are placed as they are, often with a "sharp, caustic, professional mentor" personality during the reasoning phase.
- Detailed Spatial Structuring: Produces compositions that go beyond simple descriptions, constructing deliberate meaning through compositional choices and spatial relationships.
- Extensive Training: Trained on a curated dataset of 7,203 highly detailed compositions across various tasks, genres, art styles, and emotional contexts.
- Art Style Versatility: Explicitly trained on over 40 distinct art styles, from "16-bit Pixel Art" to "Watercolor" and "Vintage Pulp Fiction", ensuring diverse output capabilities.
Good For
- Generating detailed, spatially structured image prompts from short descriptions.
- Hero-tier composition work where quality and narrative depth are paramount.
- Creating complex multi-element scenes requiring advanced spatial reasoning.
- Enhancing existing prompts with deep compositional treatment.
Comfy-UI Integration
A custom MordantPromptEnhancer Comfy-UI node is included, which handles GGUF loading, GPU VRAM auto-tuning, tokenization, and chain-of-thought parsing, simplifying local inference.