IggyLux/MN-VelvetCafe-RP-12B
IggyLux/MN-VelvetCafe-RP-12B is a 12 billion parameter merged language model developed by IggyLux, specifically fine-tuned for immersive multi-turn roleplay with a 32768 token context length. It excels at maintaining scene, position, and clothing tracking, providing balanced emotional responses, and reliably handling author's notes and system prompts. This model is optimized for visually detailed and descriptive roleplaying scenarios, aiming to prevent immersion-breaking inconsistencies.
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MN-VelvetCafe-RP-12B: A Merged Model for Immersive Roleplay
MN-VelvetCafe-RP-12B is a 12 billion parameter language model created by IggyLux through a SLERP merge of two specialized models: PocketDoc/Dans-PersonalityEngine-V1.3.0-12b and kyx0r/Neona-12B. This merge was specifically designed to enhance multi-character roleplaying experiences, focusing on visual detail and consistent narrative flow, particularly for users with VRAM limitations.
Key Capabilities
- Strong Visual Consistency: Excels at tracking and detailing scene elements, character positions, and clothing across multiple turns, preventing common immersion breaks.
- Balanced Emotional Responses: Provides nuanced emotional reactions without sudden, unprompted aggression or refusal, adapting to the narrative context.
- Reliable Instruction Following: Effectively processes author's notes and system prompts, ensuring the model adheres to user-defined guidelines.
- Adaptive Writing Style: Combines Neona's adaptability to writing styles and instruction following with Dan's focus on descriptive roleplay.
Good for
- Multi-Turn Group Roleplay: Optimized for scenarios involving two or more characters, maintaining coherence and detail.
- Visually Descriptive Narratives: Ideal for users who prioritize rich, detailed descriptions of environments, actions, and character appearances.
- Customizable Roleplay: Designed to be adaptable for both SFW and NSFW roleplay, maintaining emotional stability and instruction adherence.
- Users with 8GB VRAM: Developed with resource efficiency in mind, making it suitable for local deployment on systems with limited VRAM.