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MN-12B-Hydra-RP-RU

Name: MN-12B-Hydra-RP-RU
Rating: 2.5 (69 reviews)

🇷🇺 Нажмите, чтобы развернуть описание на русском

🌟 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.

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

🌍 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.

🎯 Key Features

Feature	Description
Languages	Russian, English
Censorship	Uncensored behavior
Roleplay	Strong character consistency and narrative depth
Instruction Following	Reliable prompt adherence
Tool Calling	Retains base Nemo capabilities
Architecture	Mistral Nemo 12B

🧩 Model Composition

The merge combines the following models:

Model	Role in merge	Weight
Pathfinder-RP-12B-RU	Base model, RP backbone	0.60
Vikhr Nemo ORPO Dostoevsky	Literary Russian depth	0.25
HERETIC Uncensored	Safety removal	0.30
Mag-Mell R1 Uncensored	Additional uncensor delta	0.20

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

💡 Usage Example

python

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)

⚙️ Merge Details

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

Core mechanism:

Trim low-magnitude deltas via density
Resolve sign conflicts
Weighted averaging of aligned parameters

Merge Configuration

yaml

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

⚠️ 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