nsfw

explicit

erp

Model card Files Files and versions

xet

Community

Instructions to use ReadyArt/Serenity-26B-A4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

llama-cpp-python

How to use ReadyArt/Serenity-26B-A4B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ReadyArt/Serenity-26B-A4B-GGUF",
	filename="Serenity-26B-A4B-HB16-Q6_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ReadyArt/Serenity-26B-A4B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ReadyArt/Serenity-26B-A4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ReadyArt/Serenity-26B-A4B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ReadyArt/Serenity-26B-A4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ReadyArt/Serenity-26B-A4B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ReadyArt/Serenity-26B-A4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ReadyArt/Serenity-26B-A4B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ReadyArt/Serenity-26B-A4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ReadyArt/Serenity-26B-A4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ReadyArt/Serenity-26B-A4B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use ReadyArt/Serenity-26B-A4B-GGUF with Ollama:
```
ollama run hf.co/ReadyArt/Serenity-26B-A4B-GGUF:Q4_K_M
```

Unsloth Studio

How to use ReadyArt/Serenity-26B-A4B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ReadyArt/Serenity-26B-A4B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ReadyArt/Serenity-26B-A4B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ReadyArt/Serenity-26B-A4B-GGUF to start chatting

How to use ReadyArt/Serenity-26B-A4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ReadyArt/Serenity-26B-A4B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ReadyArt/Serenity-26B-A4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ReadyArt/Serenity-26B-A4B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ReadyArt/Serenity-26B-A4B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ReadyArt/Serenity-26B-A4B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use ReadyArt/Serenity-26B-A4B-GGUF with Docker Model Runner:
```
docker model run hf.co/ReadyArt/Serenity-26B-A4B-GGUF:Q4_K_M
```

Lemonade

How to use ReadyArt/Serenity-26B-A4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ReadyArt/Serenity-26B-A4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Serenity-26B-A4B-GGUF-Q4_K_M

List all available models

lemonade list

Serenity-26B-A4B

⚡ v1.0

Balance achieved. Control surrendered.

Content Advisory: Mature Themes / Adult Interactions — Intended for mature audiences only.

GEMMA-4 BASED

🌊 What Is Serenity?

Born from the convergence of two lineages, Serenity-26B-A4B harmonizes the best of both worlds — the expressive depth of Melody and the immersive presence of Darkside — into a single, balanced model.

Lineage A

Melody1437

Male → Female Perspective

✦

Lineage B

Darkside

Female → Male Perspective

→

Hybrid

Serenity

⚠️ Perspective Note: Due to the merger of opposing perspective lineages, this tune may occasionally exhibit perspective confusion.

🧠 Dual Heritage: Combines Melody's male → female perspective with Darkside's female → male perspective
⚖️ Balanced Tone: Refined personality that flows naturally without overwhelming the narrative
🎭 Enhanced Depth: LoRA 80 captures nuanced behavioral patterns from both parent approaches

🧬 Synthetic Life Engine

Dataset generated using our advanced Character Engine and Emotional Engine, creating genuine life and emotional resonance in every interaction.

🎭 Character Engine

Ensures consistent personality traits, speech patterns, and behavioral logic across all contexts. Characters remain true to themselves throughout.

💓 Emotional Engine

Injects dynamic emotional states into responses, creating depth and realistic reactions that breathe life into every exchange.

✨ Quality Refinement

Automated detection and rewriting of repetitive phrases ensures fresh, high-quality dialogue in every turn.

💬 Dialogue Integrity

Advanced quote normalization ensures balanced dialogue markers, preventing formatting errors and maintaining immersion.

🧠 Training Details & Parameters

Fine-tuned using LoRA (Low-Rank Adaptation) for efficient and targeted weight adjustment, preserving the base model's capabilities while imprinting new behavioral patterns.

🔢 Epochs

Full passes through the training dataset for thorough learning

📐 LoRA Rank

Higher rank for richer adaptation and nuanced expression

Parameter	Value
Training Method	LoRA (Low-Rank Adaptation)
LoRA Rank (r)	80
Epochs	2
Trained Layers	Text layers only

🎯 Method: LoRA fine-tuning for parameter-efficient adaptation, preserving base knowledge while imprinting new behaviors
⚡ Efficiency: Only a fraction of parameters updated, keeping the model lean and responsive
🎭 Result: Maintains coherence while exhibiting the desired personality traits and interaction style

🔮 Training Process

Model weights subjected to iterative refinement during data creation. Each conversation underwent multiple checks for stability and alignment.

🔄 Multi-Turn Generation: Conversations built turn-by-turn for natural context flow
🛡️ Refusal Filtering: Automated systems detected and removed unwanted refusals
🧹 Slop Cleaning: Undesirable phrases identified and rewritten by dedicated assistant models

📚 Dataset Overview

Model trained on a specialized adult-oriented roleplay dataset with diverse scenarios and emotional contexts, drawing from the strengths of both parent lineages.

🔞 Content Rating: Strictly 18+ (Adults Only)
🎭 Focus: Mature themes, immersive roleplay, uncensored dialogue

📜 Version Notes

⚠️ Content Warning: This model can generate mature content. Use responsibly.
🧹 Clean Output: Reasoning tags and excessive markdown stripped for cleaner roleplay
🔀 Hybrid Lineage: Combines the male → female perspective of Melody1437 with the female → male perspective of Darkside into a unified, balanced model

⚙️ Configuration

🎛️ Sampler Settings

Recommended parameters for optimal output

0.92 Top-P

1.0 Temperature

0 Freq Pen

0 Presence Pen

📦 GGUF Quantizations

Available formats for local inference

Q4_K_MDecent Quality
Q5_K_MRecommended
Q6_KHigh Quality
Q8_0Near Lossless

⬇ View Repository

💖 Credits

GECFDO — Data Generation & iMatrix Quants
Sleep Deprived — Dataset Generator
FrenzyBiscuit — Fine-Tuning & Dataset Creation

🔖 License & Usage

⚠️ Usage Agreement: By using this model, you accept all terms and conditions.

📜 Built upon the Apache 2.0 license
🛡️ You accept full responsibility for all outputs generated
🔞 You confirm you are at least 18 years old
🌍 Creators bear no responsibility for how the model is used
🏡 For personal use only (non-profit/non-commercial) to the extent legally allowed