Text Generation
Transformers
Safetensors
English
Russian
mistral
mergekit
Merge
russian
uncensored
roleplay
mixtral-nemo
conversational
text-generation-inference
Instructions to use limloop/MN-12B-Hydra-RP-RU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use limloop/MN-12B-Hydra-RP-RU with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="limloop/MN-12B-Hydra-RP-RU") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("limloop/MN-12B-Hydra-RP-RU") model = AutoModelForCausalLM.from_pretrained("limloop/MN-12B-Hydra-RP-RU") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use limloop/MN-12B-Hydra-RP-RU with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "limloop/MN-12B-Hydra-RP-RU" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "limloop/MN-12B-Hydra-RP-RU", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/limloop/MN-12B-Hydra-RP-RU
- SGLang
How to use limloop/MN-12B-Hydra-RP-RU with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "limloop/MN-12B-Hydra-RP-RU" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "limloop/MN-12B-Hydra-RP-RU", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "limloop/MN-12B-Hydra-RP-RU" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "limloop/MN-12B-Hydra-RP-RU", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use limloop/MN-12B-Hydra-RP-RU with Docker Model Runner:
docker model run hf.co/limloop/MN-12B-Hydra-RP-RU
File size: 5,343 Bytes
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license: apache-2.0
base_model:
- IlyaGusev/vikhr_nemo_orpo_dostoevsky_12b_slerp
- DavidAU/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC
- Naphula/MN-12B-Mag-Mell-R1-Uncensored
- Aleteian/Pathfinder-RP-12B-RU
library_name: transformers
language:
- en
- ru
tags:
- mergekit
- merge
- russian
- uncensored
- roleplay
- mixtral-nemo
---
# MN-12B-Hydra-RP-RU
<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.
---
## 🌍 Overview
**MN-12B-Hydra-RP-RU** is an experimental merge built on top of [Mistral Nemo 12B](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407), 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](https://github.com/cg123/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
```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
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