M3.2-24B-Animus-V7.1
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This is Version 7.1 of the fine-tuned mistralai/Mistral-Small-3.2-24B-Instruct-2506. This version is a direct continuation of V7.0, created by extending the training process to **intentionally overfit** the model on its specialized dataset. The purpose is to push its adherence to the specific lore, character voices, and narrative style of the Wings of Fire universe to the absolute limit.
The goal of this model is to provide the most lore-accurate and immersive conversational experience to date. It can adopt canon character personas with high fidelity, explore alternate timelines from the books, and guide the narrative with new interactive elements.
While trained for a specific universe, previous versions have shown surprising capability in general, non-WOF roleplay. This versatility is expected to continue in V7.1, making it a flexible creative partner for various scenarios.
Training Details
Training Hardware & Procedure
This model was trained for a total of 1.8 epochs on a rented single NVIDIA H100 GPU. V7.1 is the result of continuing the V7.0 training run, with the specific goal of overfitting the model on the dataset. A QLoRA (Quantized Low-Rank Adaptation) approach was used for efficient fine-tuning, with an optimized process configured using Axolotl.
Training Data
V7.1 was created by continuing the fine-tune of V7.0 on the same refined dataset, which consists of 3,200 high-quality examples. This version includes targeted improvements to the data:
- Canon-Centric Scenarios: All roleplay scenarios are based on pivotal events from the Wings of Fire book series, exploring "what-if" outcomes. (e.g., What if Darkstalker didn't kill Arctic at that moment?). This ensures deep and lore-consistent interactions.
- Canon-Only Characters: The model was trained exclusively on canon characters from the books. AI-generated characters have been removed from the training data (except for the user's persona), leading to more authentic character portrayals.
- Enhanced Dungeon Master (DM) Role: Training data was adjusted to better capture the Dungeon Master role, encouraging the model to prompt the user with multiple-choice actions to drive the story forward. For example:
You arrive in front of Queen Scarlet. What do you do? A)... B)... C)... - Fixed Turn-Taking: The dataset was corrected to eliminate instances of consecutive AI turns, leading to a healthier learning curve and better conversational behavior.
- Improved Data Cleaning: The rigorous cleaning process was maintained, removing formatting artifacts like
**scene transitions**for cleaner, more natural prose.
Intended Use & Limitations
- Intended Use: The primary purpose of this model is for creative and roleplaying within the Wings of Fire universe. However, user feedback indicates it is also highly effective for general-purpose roleplaying.
- Limitations & Quirks:
- Performance on tasks outside of its training domain (general knowledge, coding, etc.) is not guaranteed and will likely be poor.
- Versatility: While it appears to be only a Wings of Fire tuned model, users have reported it is very capable of performing normal roleplay with other settings and characters.
- The model may "hallucinate" or generate plausible but non-canonical information, especially when pushed outside the established "what-if" scenarios.
- Content: The training data includes mature and darker themes from the Wings of Fire series, such as conflict, character death, and moral ambiguity. The model is capable of generating content reflecting these themes. As always, it is up to the user what they do with it.
- Formatting: Training data was cleaned to remove narrative artifacts like
**scene transitions**. The model should now produce cleaner prose. - Safety: This model has not undergone additional safety alignment beyond what was included in its base Mistral-Small-3.2 model. Standard responsible AI practices should be followed.
Recommended Sampler Settings
For optimal performance that balances creativity and coherence, the following default sampler settings are recommended.
Acknowledgements
- Credit to Mistral AI for the powerful Mistral-Small-3.2 architecture.
- Credit to Google for the Gemini Pro model, used in dataset generation.
- Credit to Evan Armstrong for Augmentoolkit, an invaluable tool for dataset creation.