Instructions to use ReadyArt/Dark-Scarlett-v0.125-27B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ReadyArt/Dark-Scarlett-v0.125-27B-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/Dark-Scarlett-v0.125-27B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf ReadyArt/Dark-Scarlett-v0.125-27B-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/Dark-Scarlett-v0.125-27B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf ReadyArt/Dark-Scarlett-v0.125-27B-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/Dark-Scarlett-v0.125-27B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ReadyArt/Dark-Scarlett-v0.125-27B-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/Dark-Scarlett-v0.125-27B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ReadyArt/Dark-Scarlett-v0.125-27B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/ReadyArt/Dark-Scarlett-v0.125-27B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use ReadyArt/Dark-Scarlett-v0.125-27B-GGUF with Ollama:
ollama run hf.co/ReadyArt/Dark-Scarlett-v0.125-27B-GGUF:Q4_K_M
- Unsloth Studio
How to use ReadyArt/Dark-Scarlett-v0.125-27B-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/Dark-Scarlett-v0.125-27B-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/Dark-Scarlett-v0.125-27B-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/Dark-Scarlett-v0.125-27B-GGUF to start chatting
- Pi
How to use ReadyArt/Dark-Scarlett-v0.125-27B-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/Dark-Scarlett-v0.125-27B-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/Dark-Scarlett-v0.125-27B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use ReadyArt/Dark-Scarlett-v0.125-27B-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/Dark-Scarlett-v0.125-27B-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/Dark-Scarlett-v0.125-27B-GGUF:Q4_K_M
Run Hermes
hermes
- OpenClaw new
How to use ReadyArt/Dark-Scarlett-v0.125-27B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ReadyArt/Dark-Scarlett-v0.125-27B-GGUF:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "ReadyArt/Dark-Scarlett-v0.125-27B-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use ReadyArt/Dark-Scarlett-v0.125-27B-GGUF with Docker Model Runner:
docker model run hf.co/ReadyArt/Dark-Scarlett-v0.125-27B-GGUF:Q4_K_M
- Lemonade
How to use ReadyArt/Dark-Scarlett-v0.125-27B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ReadyArt/Dark-Scarlett-v0.125-27B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Dark-Scarlett-v0.125-27B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Dark Scarlett 27B
Passion ignited. Boundaries dissolved.
chat_template_kwargs: {"enable_thinking": true, "reasoning_effort": "medium"} in SillyTavern.
Core Capabilities
Optimized for immersive adult narrative experiences
🌹 What Is Dark Scarlett?
Inspired by the spirit of Melody1437, though trained on a distinct dataset, Dark Scarlett-27B refines the core architecture with specialized scenario prompts to enhance depth and responsiveness. This model focuses on delivering a cohesive narrative experience with a distinct perspective.
- 🧠 Core Heritage: Captures the spirit of Melody1437 with diversified scenario prompts
- 🎭 Group Chat Support: Includes some training for dynamic group chat interactions
- 🎯 Perspective: Optimized for Male User → Female AI interactions
- 📖 Narrative Flow: Improved context retention with longer conversation threads in select scenarios
🧬 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
Optimized rank for balanced adaptation and efficiency
| Parameter | Value |
|---|---|
| Training Method | LoRA (Low-Rank Adaptation) |
| LoRA Rank (r) | 32 |
| Epochs | 2 |
| Trained Layers | Text layers only |
| Experimental Feature | Group Chat Support |
- 🎯 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
- 🎪 Group Chat Training: Includes some training data focused on multi-user group dynamics
📚 Dataset Overview
Model trained on a specialized adult-oriented roleplay dataset with diverse scenarios and emotional contexts, inspired by the spirit of Melody1437, though distinct in composition.
- 🔞 Content Rating: Strictly 18+ (Adults Only)
- 📊 Dataset Size: Currently contains 4,500 specialized prompts.
- 📝 Conversation Depth: Portions include extended conversations for enhanced roleplay continuity.
- 🎭 Focus: Mature themes, immersive roleplay, uncensored dialogue
- 👥 Perspective: Male (User) → Female (AI)
- 🧹 Formatting: Removed Em dashes, asterisks, and markdown formatting from the dataset for cleaner output.
📜 Version Notes
- ⚠️ Content Warning: This model can generate mature content. Use responsibly.
- 🧪 Test Build: This is an experimental release (v0.125) and not the final version.
- 📉 Data Limitations: Trained on a curated dataset (4,500 prompts); performance may vary.
- 🧹 Clean Output: Reasoning tags, Em dashes, asterisks, and markdown formatting stripped for cleaner roleplay
- 🔀 Core Identity: Trained on the spirit of Melody1437, but utilizes a unique dataset
- 🎪 Experimental: Includes some training for group chat scenarios
⚙️ Configuration
🎛️ Sampler Settings
Recommended parameters for optimal output
📦 GGUF Quantizations
Available formats for local inference
- Q4_K_MDecent Quality
- Q5_K_MRecommended
- Q6_KHigh Quality
- Q8_0Near Lossless
💖 Credits
-
GECFDO — Dataset Generation
-
FrenzyBiscuit — Fine-Tuning & Dataset Creation
🔖 License & Usage
- 📜 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
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