Instructions to use lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF", dtype="auto")

llama-cpp-python

How to use lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF",
	filename="qwen2.5-7b-instruct-abliterated-v2-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-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 lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-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 lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-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 lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-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 lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF:Q4_K_M

SGLang

How to use lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF:Q4_K_M
```

Unsloth Studio

How to use lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-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 lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-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 lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF to start chatting

How to use lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-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": "lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-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 lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-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 lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-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 "lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-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 lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

Qwen 2.5 7B Instruct Abliterated v2 (Spanish Friendly) - GGUF 🚀

Este repositorio contiene el modelo Qwen 2.5 7B Instruct (v2), liberado de censura mediante la avanzada técnica Abliterated por huihui-ai, y convertido al formato ligero GGUF (Cuantización Q4_K_M) por Lumen Models.

Está optimizado para ejecutarse de forma 100% local, privada y eficiente en ordenadores comunes (8GB - 16GB de RAM) utilizando programas gratuitos como LM Studio, Ollama, Jan o Faraday.

🔥 Características Destacadas:

100% Uncensored / Abliterated: Libre de filtros morales, sermones o bloqueos corporativos. Responde de forma directa y madura a cualquier solicitud de escritura creativa, juegos de rol (RP), análisis de textos complejos o investigación sin restricciones.
Spanish Native Performance: Una de las mejores arquitecturas del mundo (Qwen 2.5) con una fluidez, vocabulario, gramática y comprensión espectaculares en español nativo.
Formato Optimizado Q4_K_M: El método de cuantización más recomendado por la comunidad en Reddit y Hugging Face. Reduce el peso del modelo drásticamente manteniendo casi intacta la inteligencia del modelo original.

💻 ¿Cómo usarlo localmente?

Descarga el archivo .gguf de la pestaña Files and versions.
Ábrelo en tu software favorito:
- LM Studio: Arrastra el archivo a la carpeta de modelos y cárgalo.
- Ollama: Crea un Modelfile apuntando a este archivo.
¡Disfruta de una IA inteligente, rápida y totalmente libre en tu PC!

⚠️ Disclaimer (Descargo de responsabilidad)

Este modelo se comparte exclusivamente con fines de investigación, literatura creativa, testeo de ciberseguridad y desarrollo local privado. Lumen Models actúa únicamente como distribuidor del formato optimizado GGUF y no se hace responsable de las opiniones, sesgos, contenidos generados o del uso final que los usuarios den a las respuestas del modelo.

Downloads last month: 663

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for lumen-models/Qwen2.5-7B-Instruct-abliterated-v2-Q4_K_M-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

huihui-ai/Qwen2.5-7B-Instruct-abliterated-v2

Quantized

(13)

this model