Redemption_Wind_24B
| Property | Value |
|---|---|
| Base Model | Mistral 24B |
| License | Apache 2.0 |
| Formats Available | FP16, GGUF, GPTQ, FP8, Q4_0 |
| Author | SicariusSicariiStuff |
What is Redemption_Wind_24B?
Redemption_Wind_24B is a strategically undercooked fine-tune of the Mistral 24B base model, specifically designed for the fine-tuning community. With an intentionally high loss value of 8.0, this model serves as an adaptable foundation for further development while maintaining impressive coherence and capabilities.
Implementation Details
The model implements ChatML formatting without introducing additional tokens (with minor exceptions). It incorporates high-quality private instruction data, avoiding synthetic content from common AI assistants. The implementation includes specialized datasets for creative writing and roleplay, with particular attention to character card adherence.
- ChatML-compatible architecture with minimal token modifications
- Private instruction dataset with robust markdown understanding
- Specialized creative writing and roleplay datasets up to 16k tokens
- Multiple quantization options for different deployment scenarios
Core Capabilities
- Enhanced creative writing and storytelling abilities
- Strong character card adherence for roleplay scenarios
- Minimal refusal behavior while maintaining safety features
- Flexible foundation for further fine-tuning
- Multiple quantization options for various deployment scenarios
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its intentionally high loss value of 8.0, making it particularly suitable for further fine-tuning while maintaining coherent outputs. It combines creative capabilities with technical adaptability, making it valuable for both end-users and model developers.
Q: What are the recommended use cases?
Primary use cases include: base for further fine-tuning, foundation for model merging, roleplay applications, creative writing tasks, and general assistant functions. The model performs particularly well in scenarios requiring creative output or character adherence.
