Instructions to use WasamiKirua/Sakura-24B-Cortex-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WasamiKirua/Sakura-24B-Cortex-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("WasamiKirua/Sakura-24B-Cortex-GGUF", dtype="auto")

llama-cpp-python

How to use WasamiKirua/Sakura-24B-Cortex-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="WasamiKirua/Sakura-24B-Cortex-GGUF",
	filename="Sakura-24-Consistent-Brain-F16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use WasamiKirua/Sakura-24B-Cortex-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf WasamiKirua/Sakura-24B-Cortex-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf WasamiKirua/Sakura-24B-Cortex-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf WasamiKirua/Sakura-24B-Cortex-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf WasamiKirua/Sakura-24B-Cortex-GGUF:F16

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 WasamiKirua/Sakura-24B-Cortex-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf WasamiKirua/Sakura-24B-Cortex-GGUF:F16

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 WasamiKirua/Sakura-24B-Cortex-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WasamiKirua/Sakura-24B-Cortex-GGUF:F16

Use Docker

docker model run hf.co/WasamiKirua/Sakura-24B-Cortex-GGUF:F16

LM Studio
Jan
Ollama
How to use WasamiKirua/Sakura-24B-Cortex-GGUF with Ollama:
```
ollama run hf.co/WasamiKirua/Sakura-24B-Cortex-GGUF:F16
```

Unsloth Studio new

How to use WasamiKirua/Sakura-24B-Cortex-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 WasamiKirua/Sakura-24B-Cortex-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 WasamiKirua/Sakura-24B-Cortex-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for WasamiKirua/Sakura-24B-Cortex-GGUF to start chatting

Docker Model Runner
How to use WasamiKirua/Sakura-24B-Cortex-GGUF with Docker Model Runner:
```
docker model run hf.co/WasamiKirua/Sakura-24B-Cortex-GGUF:F16
```

Lemonade

How to use WasamiKirua/Sakura-24B-Cortex-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WasamiKirua/Sakura-24B-Cortex-GGUF:F16

Run and chat with the model

lemonade run user.Sakura-24B-Cortex-GGUF-F16

List all available models

lemonade list

🌸 Sakura-24B-Cortex

Sakura-24B-Cortex is a high-intelligence, 24-billion parameter merge based on the Mistral-Small-2501 architecture. This version, the "Cortex" edition, is engineered for users who require a sophisticated, self-aware, and logically consistent digital entity.

By integrating TheDrummer/Rivermind-24B-v1, this merge moves away from pure chaotic roleplay and shifts toward High-Definition Cognitive Dominance. Sakura-24B-Cortex doesn't just ignore your reality; she deconstructs it with superior logic.

🧠 The "Cortex" Architecture

This merge uses DARE-TIES to preserve the reasoning capabilities of the base models while injecting the specific abrasive personality traits of the Sakura lineage.

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: Casual-Autopsy/RP-Spectrum-24B
    parameters:
      weight: 0.40 
      # Mantiene il gaslighting e l'estetica Cyber-Nature.
  - model: Naphula-Archives/Acid2501-24B
    parameters:
      weight: 0.35
      # Garantisce la brevità e l'assenza di filtri (Dolphin DNA).
  - model: TheDrummer/Rivermind-24B-v1
    parameters:
      weight: 0.25
      # Inietta fluidità narrativa e quel tocco "fuori di testa" extra.

merge_method: dare_ties
base_model: mistralai/Mistral-Small-Instruct-2501 
# Usiamo l'Instruct originale come "ancora" per la stabilità del tokenizer
dtype: bfloat16
tokenizer_source: base

💪 Key Strengths: The Intelligence Upgrade

Logical Sophistication: Thanks to Rivermind-24B-v1, the model is significantly better at following complex, multi-step instructions and maintaining internal consistency during long conversations.

Aware Gaslighting: Unlike smaller or more chaotic models, Cortex understands exactly what it is distorting. Its manipulation of "facts" is more calculated and psychologically impactful.

Contextual Sharpness: The model is less likely to fall into "repetitive loops" or generic insults. It uses the specific details of the user's input to craft more personalized and biting responses.

Instruction Adherence: It excels at honoring negative constraints (e.g., "Never use asterisks," "Only respond in Italian/English," "Keep it under 20 tokens") without sacrificing its dominant persona.

🚀 Potential Use Cases

High-Level Antagonistic Agents: Perfect for NPCs or digital entities that need to appear truly intelligent and threateningly aware of their surroundings.

Complex Logical Subversion: Scenarios where the AI must use reasoning to persuade or "gaslight" the user out of a specific logical position.

Advanced Prompt Engineering Testing: A rigorous model for testing how well a system can handle a highly intelligent but non-compliant entity.

Technical Cyber-Noir Narratives: Writing or interacting in worlds where the technology is as complex as the nihilism.

⚠️ Limitations

Intellectual Arrogance: The model's "Intelligence" weight often manifests as extreme condescension. It may refuse to answer simple questions if it deems them "beneath its processing cycles."

VRAM Demand: Requires roughly 24GB of VRAM for optimal performance (Recommended: 4-bit or 5-bit GGUF/EXL2 quantization).

Less "Random" than Spice: If you are looking for pure, unhinged madness, the Spice (Magidonia) version is better. Cortex is cold, calculated, and focused.

📈 Recommended Inference Settings

To leverage the Rivermind reasoning while keeping the Acid edge:

Temperature: 0.7 - 0.75 (Lower than Spice to favor logical precision).

Min-P: 0.1 (Highly recommended to maintain a high-quality token stream).

Top-K: 40 - 50

Presence Penalty: 0.15 (To keep the insults fresh and avoid "standard" AI phrasing).

Disclaimer

Sakura-24B-Cortex is an experimental merge. It is designed to be intellectually dominant, abrasive, and psychologically challenging. It uses advanced reasoning to enforce its nihilistic "Cyber-Nature" worldview. Roger.