Instructions to use void-gryph/perfectdeliberate-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use void-gryph/perfectdeliberate-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="void-gryph/perfectdeliberate-GGUF",
	filename="perfectdeliberate.Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use void-gryph/perfectdeliberate-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf void-gryph/perfectdeliberate-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf void-gryph/perfectdeliberate-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf void-gryph/perfectdeliberate-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf void-gryph/perfectdeliberate-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 void-gryph/perfectdeliberate-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf void-gryph/perfectdeliberate-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 void-gryph/perfectdeliberate-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf void-gryph/perfectdeliberate-GGUF:Q4_K_M

Use Docker

docker model run hf.co/void-gryph/perfectdeliberate-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use void-gryph/perfectdeliberate-GGUF with Ollama:
```
ollama run hf.co/void-gryph/perfectdeliberate-GGUF:Q4_K_M
```

Unsloth Studio new

How to use void-gryph/perfectdeliberate-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 void-gryph/perfectdeliberate-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 void-gryph/perfectdeliberate-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for void-gryph/perfectdeliberate-GGUF to start chatting

Docker Model Runner
How to use void-gryph/perfectdeliberate-GGUF with Docker Model Runner:
```
docker model run hf.co/void-gryph/perfectdeliberate-GGUF:Q4_K_M
```

Lemonade

How to use void-gryph/perfectdeliberate-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull void-gryph/perfectdeliberate-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.perfectdeliberate-GGUF-Q4_K_M

List all available models

lemonade list

PerfectDeliberate - GGUF Ultimate Edition 🏭

Este repositorio ofrece la colección en formato GGUF del modelo original PerfectDeliberate. Optimizados para inferencia con poca memoria.

� Tabla de Comparativa de Cuantizaciones

Versión	Tipo	Peso	Calidad	Uso Recomendado
Q4_K_M	Medium	~5-7GB	Basica	Balance / Velocidad
Q5_K_M	Large	~8-10GB	Alta	Calidad
Q8_0	Ultra	Original	Alta	Calidad

⚙️ Guía de Optimización y Comparativa de Parámetros

Para obtener los mejores resultados con esta versión GGUF, se recomiendan los siguientes ajustes:

1. 🎚️ Comparativa de CFG (Classifier Free Guidance)

CFG Scale	Efecto en GGUF	Resultado Visual
1.0 - 3.5	Suave / Realista	Menos contraste, ideal para estilos fotográficos.
4.0 - 6.5	Recomendado	Balance perfecto entre fidelidad al prompt y detalle.
7.0 - 9.0	Estilizado	Colores más saturados y bordes más definidos.

2. ⚡ Comparativa de Pasos (Sampling Steps)

Pasos	Rendimiento	Nivel de Detalle
15 - 20	Ultra Rápido	Bocetos rápidos o previsualizaciones.
25 - 35	Óptimo	El equilibio para GGUF con casi cero ruido.
40+	Estándar	Máximo refinamiento de texturas complejas.

3. 🌫️ Comparativa de Denoise (Solo para i2i / Hires Fix)

0.35 - 0.45: Mantiene la estructura original pero con limpieza de artefactos.
0.50 - 0.65: El rango ideal para añadir detalle sin deformar el sujeto.
0.70+: Cambio significativo de composición (usar con precaución).

👤 Créditos y Atribución

Autor Original: Desync
Cuantización Experta: void-gryph

🚀 Despliegue en ComfyUI

Archivo GGUF: Mover a ComfyUI/models/unet/
Nodos Requeridos: Es necesario tener instalado ComfyUI-GGUF.
Componentes Originales: Use el CLIP y VAE incluidos en este repo para máxima fidelidad (extraídos sin prefijos de contenedor).

📝 Nota del Autor Original

I try to pin images that stand out / i personally like every 1-3 weeks So if i unpin yours doesnt mean that i dont like it anymore just trying to keep a fresh rotation of images without completly filling the start page with pins <3

Join the Discord for questions / sharing pictures :) : https://discord.gg/gcJqAKQ5Af

If you enjoy my models and want to support me, you can buy me a coffee! (Since my patreon got disabled like a year ago)

🛠️ Recommended Settings:

Resolution: best results with 832x1216 //960x1440// 1024x1024 // 1024x1536

CFG Scale: 5–8

Sampling Method: Euler a or DPM++ 2M Karras

Steps: 20–50

GGUF Quantizer - Comprimiendo la inteligencia artificial, píxel a píxel.

Downloads last month: 74

GGUF

Model size

3B params

Architecture

sdxl

Hardware compatibility

4-bit

5-bit

8-bit

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