MN-12B-Hydra-RP-RU-GGUF

MN-12B-Hydra-RP-RU

Original model

<details> <summary>🇷🇺 Нажмите, чтобы развернуть описание на русском</summary>

🌟 О модели

MN-12B-Hydra-RP-RU — экспериментальный merge на базе Mistral Nemo 12B, сочетающий:

• 🎭 Сильные ролевые способности
• 📚 Глубокий литературный русский язык
• 🔓 Снятую цензуру

Модель собрана методом TIES-merging, что позволяет объединять веса нескольких моделей с минимальными конфликтами между параметрами.

🎯 Особенности

• Основной язык — русский
• Хорошо держит персонажей и контекст
• Следует инструкциям
• Сохраняет возможности базового Nemo
• Не проходила дополнительного обучения после слияния

⚠️ Важно

Uncensored-характер модели означает, что она может генерировать контент, который некоторые пользователи сочтут неподобающим.

</details>

High-quality TIES merge based on Mistral Nemo 12B, optimized for roleplay, strong Russian language capabilities, and uncensored behavior.

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🌍 Overview

MN-12B-Hydra-RP-RU is an experimental merge built on top of Mistral Nemo 12B, combining strengths from multiple fine-tuned models:

• 🎭 Advanced roleplay capability from Pathfinder-RP
• 📚 Deep Russian language fluency inspired by Vikhr + Dostoevsky-style tuning
• 🔓 Reduced safety filtering via uncensored components

The merge was created using TIES merging, which allows combining model deltas while minimizing destructive interference between weights.

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🎯 Key Features

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🧩 Model Composition

The merge combines the following models:

Weights shown before normalization (final weights are normalized to sum = 1).

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💡 Usage Example

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "limloop/MN-12B-Hydra-RP-RU"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

prompt = "You are a medieval innkeeper. Greet the traveler!"
messages = [{"role": "user", "content": prompt}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)

outputs = model.generate(inputs, max_new_tokens=512, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

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⚙️ Merge Details

Built using mergekit with the TIES method (Trim, Elect Sign, Merge).

Core mechanism:

1. Trim low-magnitude deltas via density
2. Resolve sign conflicts
3. Weighted averaging of aligned parameters

Merge Configuration

models:
  - model: Aleteian/Pathfinder-RP-12B-RU
    weight: 0.6
  - model: IlyaGusev/vikhr_nemo_orpo_dostoevsky_12b_slerp
    weight: 0.25
    density: 0.9
  - model: DavidAU/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC
    weight: 0.3
    density: 0.9
  - model: Naphula/MN-12B-Mag-Mell-R1-Uncensored
    weight: 0.2
    density: 0.9

merge_method: ties
parameters:
  epsilon: 0.01
  normalize: true
base_model: Aleteian/Pathfinder-RP-12B-RU
dtype: bfloat16
tokenizer:
  source: base

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⚠️ Known Characteristics

• No additional post-merge fine-tuning
• May switch to English on complex reasoning tasks
• Uncensored components allow generation of explicit or controversial content