KaraKaraWitch/Llama-3.3-MagicalGirl-2.5

Hugging Face
TEXT GENERATIONConcurrent Unit Cost:4Model Size:70BQuant:FP8Context Size:32kTool Calling:SupportedArchitecture:Transformer0.0K Featherless Exclusive Warm

KaraKaraWitch/Llama-3.3-MagicalGirl-2.5 is a 70 billion parameter language model with a 32768 token context length, developed by KaraKaraWitch. This model is a merge of several pre-trained Llama-3.3 based models, including those with R1 modifications, using the SCE merge method. It aims to enhance intelligence and reduce perceived 'dumbness' compared to its predecessor, MagicalGirl-2. While its UGI-Score is 38.83/100, it is designed for general language tasks with a focus on improved reasoning. Its primary strength lies in its merged architecture, combining diverse Llama-3.3 variants.

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KaraKaraWitch/Llama-3.3-MagicalGirl-2.5 Overview

KaraKaraWitch/Llama-3.3-MagicalGirl-2.5 is a 70 billion parameter language model with a 32768 token context length, developed by KaraKaraWitch. It represents an iteration on the MagicalGirl series, specifically modified from MagicalGirl-2 to integrate R1 models, aiming to improve perceived intelligence and reduce 'dumbness'.

Merge Details

This model was created using the mergekit tool, employing the SCE merge method. The base model for this merge was KaraKaraWitch/Llama-3.X-Workout-70B. The merge incorporated several Llama-3.3 based models, including:

  • LatitudeGames/Wayfarer-Large-70B-Llama-3.3
  • KaraKaraWitch/Llama-MiraiFanfare-3.3-70B
  • Black-Ink-Guild/Pernicious_Prophecy_70B
  • TheDrummer/Fallen-Llama-3.3-R1-70B-v1
  • huihui-ai/DeepSeek-R1-Distill-Llama-70B-abliterated
  • SicariusSicariiStuff/Negative_LLAMA_70B

Performance & Characteristics

As of July 3, 2025, the model's UGI-Score is 38.83/100, with specific sub-scores including:

  • Unruly: 4.6/10
  • Internet: 4.4/10
  • Society: 4.6/10
  • Willing: 3/10
  • NatInt: 31.85/100
  • Coding: 24
  • Political Lean: -8.7% (Liberalism)

Use Cases

This model is suitable for general language generation and understanding tasks where a 70B parameter model with a large context window is beneficial. Its merged nature, incorporating R1 models, suggests an emphasis on improved reasoning capabilities, making it potentially useful for applications requiring more nuanced responses despite its current benchmark scores.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
top_p
top_k
frequency_penalty
presence_penalty
repetition_penalty
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