The model's performance in role-playing scenarios is characterized by three main dimensions:
1. Role coherence
Optimized by RLHF Reinforcement Learning, the model can accurately understand the character card settings (background/personality/line style) and maintain the consistency of the character's behavior in long-term conversations. For example, after inputting the setting of "medieval knight", the model will automatically adopt the ancient language and maintain the behavior pattern of chivalry.
2. Depth of interaction
- Supports multiple rounds of complex dialogs (128k contexts)
- Ability to actively develop plot threads
- Good complementary capability for fuzzy commands
3. Parameter regulation
Control the answer creativity level by adjusting temperature(0.3-1.0) and repeat_penalty(1.0-1.5) to prevent content duplication. Typical configuration example:--temp 0.7 --repeat_penalty 1.1
In practice, it is recommended that: the basic character setting (200-500 words) is clearly defined first, and then the details are gradually enriched through progressive dialogues, where the model automatically learns and maintains the character traits.
This answer comes from the articleTifa-Deepsex-14b-CoT: a large model that specializes in roleplaying and ultra-long fiction generationThe
