Mawdistical/Feral-Allura-70B

Warm
Public
70B
FP8
32768
License: llama3.3
Hugging Face
Overview

What the fuck is this model about?

Feral-Allura-70B is a 70 billion parameter finetuned language model developed by Mawdistical, building upon TheSkullery's Unnamed-Exp-70b-v0.7.A. It features a substantial 32768 token context length, allowing for extended and complex interactions. The model is described as a "monstrous fusion where bestial wrath collides with the fractured delirium of the human mind," indicating a focus on generating content that is intense, raw, and potentially explicit.

What makes THIS different from all the other models?

This model's primary differentiator lies in its specialized finetuning, aiming for a unique blend of "bestial wrath" and "fractured delirium." Unlike general-purpose LLMs, Feral-Allura-70B is crafted to produce content with a distinct, untamed, and possibly dark or explicit narrative style. It is not designed for typical factual recall or benign conversational tasks but rather for specific creative or thematic applications that leverage its unconventional persona. The model also provides specific recommendations for temperature and dynamic temperature settings to optimize its output.

Should I use this for my use case?

Use this model if:

  • You require a language model capable of generating highly unconventional, intense, or explicit content.
  • Your application involves creative writing, roleplay, or narrative generation with themes of "bestial wrath" or "fractured delirium."
  • You are looking for a model with a distinct, untamed, and non-mainstream persona.
  • You are comfortable with and specifically seeking content that carries an "Explicit Content Warning."

Do NOT use this model if:

  • You need a general-purpose assistant for factual queries, summarization, or standard creative tasks.
  • Your application requires strictly safe, non-explicit, or family-friendly content.
  • You are sensitive to or wish to avoid themes of "bestial wrath" or "fractured delirium."
  • You prioritize models optimized for reasoning, coding, or traditional instruction-following over thematic specialization.