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.

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 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: 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

limloop/MN-12B-Faun-RP-RU-GGUF

MN-12B-Faun-RP-RU

🌟 О модели

🎯 Особенности

⚠️ Важно