Instructions to use limloop/MN-12B-Faun-RP-RU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use limloop/MN-12B-Faun-RP-RU with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="limloop/MN-12B-Faun-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-Faun-RP-RU")
model = AutoModelForCausalLM.from_pretrained("limloop/MN-12B-Faun-RP-RU", device_map="auto")
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 Settings

vLLM

How to use limloop/MN-12B-Faun-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-Faun-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-Faun-RP-RU",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/limloop/MN-12B-Faun-RP-RU

SGLang

How to use limloop/MN-12B-Faun-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-Faun-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-Faun-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-Faun-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-Faun-RP-RU",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use limloop/MN-12B-Faun-RP-RU with Docker Model Runner:
```
docker model run hf.co/limloop/MN-12B-Faun-RP-RU
```

MN-12B-Faun-RP-RU / README.md

limloop

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	---
	license: apache-2.0
	base_model:
	- ReadyArt/Forgotten-Safeword-12B-v4.0
	- limloop/MN-12B-Hydra-RP-RU
	- SicariusSicariiStuff/Impish_Bloodmoon_12B
	library_name: transformers
	language:
	- en
	- ru
	tags:
	- mergekit
	- merge
	- russian
	- uncensored
	- roleplay
	- mixtral-nemo
	---

	# MN-12B-Faun-RP-RU

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

	## 🌟 О модели

	MN-12B-Faun-RP-RU — улучшенный merge на базе Mistral Nemo 12B, развивающий идеи Hydra и ориентированный на:

	* 🎭 Более стабильный и выразительный roleplay
	* 📚 Улучшенный русский язык
	* 🧠 Расширенный словарный запас, включая сложные и NSFW-темы
	* 🔓 Практически отсутствующую цензуру

	Модель собрана методом TIES-merging и не проходила дополнительного обучения после слияния.

	## 🎯 Особенности

	* Основной фокус — русский язык
	* Лучше удерживает персонажей и стиль диалога
	* Более богатая и вариативная генерация
	* Улучшенная стабильность на длинных контекстах (проверено до ~8192 токенов)
	* Следует инструкциям, но может добавлять дисклеймеры на чувствительные запросы

	## ⚠️ Важно

	Модель сохраняет uncensored-характер, однако в некоторых случаях может добавлять предупреждения о неподходящем контенте при прямых запросах.
	При этом генерация не блокируется и продолжается после дисклеймера.

	</details>

	High-quality TIES merge based on Mistral Nemo 12B, focused on improved Russian fluency, stronger roleplay, richer vocabulary, and stable long-context performance.

	---

	## 🌍 Overview

	MN-12B-Faun-RP-RU is an evolution of the Hydra-style merge, designed to push further in roleplay quality, language richness, and generation stability.

	Key improvements include:

	* 📚 Better Russian
	* 🎭 More consistent and immersive roleplay behavior
	* 🧠 Expanded vocabulary, including expressive and NSFW domains
	* 🔁 More stable handling of long conversations (tested up to ~8k tokens)

	The model may occasionally produce safety disclaimers when prompted directly for sensitive content, but generation continues normally afterward.

	Built using TIES merging, which minimizes destructive interference between merged model weights.

	---

	## 🎯 Key Features

	\| Feature \| Description \|
	\| ------------------------- \| ----------------------------------------------- \|
	\| Languages \| Russian, English \|
	\| Censorship \| Mostly uncensored (with occasional disclaimers) \|
	\| Roleplay \| Improved consistency and immersion \|
	\| Instruction Following \| Strong \|
	\| Vocabulary \| Expanded, including NSFW domains \|
	\| Context Length \| Stable up to ~8192 tokens \|
	\| Architecture \| Mistral Nemo 12B \|

	---

	## 🧩 Model Composition

	The merge combines the following models:

	\| Model \| Role in merge \| Weight \|
	\| ------------------------------- \| ------------------- \| ------ \|
	\| MN-12B-Hydra-RP-RU \| Base / foundation \| 0.60 \|
	\| Impish_Bloodmoon_12B \| RP + style boost \| 0.25 \|
	\| Forgotten-Safeword-12B-v4.0 \| Uncensored behavior \| 0.10 \|

	Weights shown before normalization.

	---

	## 💡 Usage Example

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch

	model_name = "limloop/MN-12B-Faun-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 mysterious forest faun speaking in poetic Russian."
	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/arcee-ai/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: limloop/MN-12B-Hydra-RP-RU
	parameters:
	weight: 0.6

	- model: SicariusSicariiStuff/Impish_Bloodmoon_12B
	parameters:
	weight: 0.25
	density: 0.9

	- model: ReadyArt/Forgotten-Safeword-12B-v4.0
	parameters:
	weight: 0.1
	density: 0.6

	merge_method: ties
	parameters:
	epsilon: 0.01
	normalize: true

	base_model: limloop/MN-12B-Hydra-RP-RU
	dtype: bfloat16

	tokenizer:
	source: "base"
	```

	---

	## ⚠️ Known Characteristics

	* No post-merge fine-tuning
	* May produce safety disclaimers before sensitive outputs
	* Occasionally switches to English in complex reasoning
	* Stronger stylistic bias in roleplay compared to Hydra