🌟 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
Table with columns: Feature, Description| 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:
Table with columns: Model, Role in merge, Weight| 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
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
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