

Tifa Deepsex 14b CoT GGUF Q4
Overview :
This product is a deeply optimized model based on the Deepseek-R1-14B architecture. By injecting a vast amount of novel and role-playing data, combined with chain-of-thought (CoT) training strategies, it significantly enhances the capabilities of role-playing, novel text generation, and long-term contextual coherence. Key advantages include strong contextual consistency, a rich vocabulary for role-playing, and support for complex logical reasoning. This model is tailored for scenarios that require deep creativity and dialogue, such as novel creation and scriptwriting. It is licensed under Apache 2.0 and is currently available for free use, pending adherence to copyright agreements.
Target Users :
This model is ideal for creators and developers needing to engage in long text creation, role-playing dialogues, and complex logical reasoning, such as novelists, scriptwriters, game designers, and professionals requiring creative writing. It offers in-depth text generation and dialogue support, assisting users in better constructing stories and characters.
Use Cases
Novelists use this model to generate plotlines and character dialogues, enhancing their creative efficiency.
Scriptwriters leverage the model for script creation, enriching character expressiveness.
Game developers utilize the model to generate game narratives and character interactions.
Features
Significantly improve contextual relevance while reducing irrelevant responses.
Eliminate code-switching between Chinese and English and enhance language expression.
Expand specialized vocabulary to deepen role-playing dialogue.
Minimize rejection occurrences and optimize generation quality.
Support 16k ultra-long contextual training to enhance text coherence.
How to Use
Visit the Hugging Face model page to download the model files.
Select the appropriate quantization version based on your needs (e.g., GGUF-Q4).
Load the model using a compatible inference framework or tool.
Follow the official example templates to construct input prompts, ensuring correct formatting.
Adjust generation parameters (such as temperature and repetition penalty) to optimize output.
Run the model to generate text and perform any necessary editing or integration.
Adhere to copyright agreements and use the generated content responsibly.
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