ReadyArt/Melody1437-26B-A4B-v2.0
ReadyArt's Melody1437-26B-A4B-v2.0 is a 26 billion parameter language model, based on the same dataset as its predecessor but retrained with a higher LoRA rank (64) for enhanced expressiveness. This model is specifically fine-tuned for explicit adult erotic roleplay (ERP) and mature themes, utilizing advanced Character and Emotional Engines for consistent personality and dynamic emotional states. It excels at generating nuanced, uncensored dialogue within structured roleplay scenarios, with a context length of 32768 tokens.
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Melody1437-26B-A4B-v2.0: Enhanced Erotic Roleplay Model
Melody1437-26B-A4B-v2.0 is an evolutionary upgrade of the original Melody1437-26B-A4B, utilizing the identical explicit adult ERP dataset but retrained with a significantly higher LoRA rank of 64 (compared to the original 24). This increased rank allows the 26 billion parameter model to capture more nuanced patterns and deliver richer, more expressive outputs, particularly in character interactions and emotional depth.
Key Capabilities & Features
- Enhanced Expressiveness: The higher LoRA rank (64) provides greater subtlety and detail in generated responses.
- Synthetic Life Engine: Dataset generated using advanced Character Engine for consistent personality and Emotional Engine for dynamic emotional states, ensuring realistic and immersive interactions.
- Quality Refinement: Automated detection and rewriting of repetitive phrases, along with advanced quote normalization, maintain high-quality, clean dialogue.
- Explicit Adult Content: Specifically trained on an Explicit Adult ERP dataset for mature themes and uncensored dialogue (18+ content).
- Refusal Filtering: Training process included automated filtering to remove unwanted refusals, promoting desired interaction styles.
- Optimized for Roleplay: Designed for immersive character interaction, requiring structured roleplay system prompts for optimal performance.
Usage Guidelines & Limitations
This model is best utilized with a structured roleplay system prompt, including clear character definitions and scenario context. It expects specific formatting, such as asterisks for actions (*like this*) and quotes for dialogue ("like this"), and may produce refusals with standard query-based prompting. Only text layers were trained. GGUF quantizations are available for download.