Aratako/MistralPrism-24B

Hugging Face
TEXT GENERATIONConcurrency Cost:2Model Size:24BQuant:FP8Ctx Length:32kPublished:Jun 8, 2025License:mitArchitecture:Transformer0.0K Open Weights Warm

Aratako/MistralPrism-24B is a 24 billion parameter language model developed by Aratako, based on mistralai/Mistral-Small-3.1-24B-Instruct-2503. This model is specifically enhanced for role-playing scenarios through a merge of multiple overseas models. It is optimized for detailed character role-play and interactive dialogue, supporting a context length of 32768 tokens.

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Overview

Aratako/MistralPrism-24B is a 24 billion parameter language model developed by Aratako, built upon the mistralai/Mistral-Small-3.1-24B-Instruct-2503 base model. It has been further refined for role-playing applications by merging it with several other models, including AlexHung29629/Mistral-Small-3.1-24B-Instruct-2503-text, TheDrummer/Cydonia-24B-v3, LatitudeGames/Harbinger-24B, Gryphe/Pantheon-RP-1.8-24b-Small-3.1, aixonlab/Eurydice-24b-v3, and Aratako/Mistral-Small-3.1-24B-RP.

Key Capabilities

  • Enhanced Role-Playing: Specifically designed and optimized for engaging in detailed character role-play, allowing users to define character settings and dialogue situations via system prompts.
  • Context Length: Supports a context length of 32,768 tokens, making it suitable for longer, more complex role-playing narratives, though long context performance is noted as untested.
  • Chat Template Adherence: Utilizes a specific chat template (<s>[SYSTEM_PROMPT]system prompt[/SYSTEM_PROMPT][INST]user message 1[/INST]assistant message 1</s>[INST]user message 2[/INST]) for optimal performance, compatible with tokenizer.apply_chat_template.

Intended Use Cases

  • Interactive Storytelling: Ideal for applications requiring dynamic and immersive character interactions.
  • Role-Playing Games (RPGs): Can serve as a robust engine for generating character responses in text-based RPGs.
  • Creative Writing Assistance: Useful for scenarios where consistent character voice and detailed situational responses are needed.

Limitations

  • The model's vision capabilities have been removed, meaning it does not support image inputs.
  • While trained with a 32k context length, its performance with long contexts is currently unverified.
  • It is suggested to use the model with a low temperature (e.g., 0.15 or lower) for better output quality, similar to its base model, though this is also unverified for MistralPrism-24B itself.