Instructions to use risataim/Pentest_AI_gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use risataim/Pentest_AI_gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="risataim/Pentest_AI_gguf",
	filename="Pentest_AI.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 risataim/Pentest_AI_gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf risataim/Pentest_AI_gguf
# Run inference directly in the terminal:
llama-cli -hf risataim/Pentest_AI_gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf risataim/Pentest_AI_gguf
# Run inference directly in the terminal:
llama-cli -hf risataim/Pentest_AI_gguf

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 risataim/Pentest_AI_gguf
# Run inference directly in the terminal:
./llama-cli -hf risataim/Pentest_AI_gguf

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 risataim/Pentest_AI_gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf risataim/Pentest_AI_gguf

Use Docker

docker model run hf.co/risataim/Pentest_AI_gguf

LM Studio
Jan
Ollama
How to use risataim/Pentest_AI_gguf with Ollama:
```
ollama run hf.co/risataim/Pentest_AI_gguf
```

Unsloth Studio new

How to use risataim/Pentest_AI_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 risataim/Pentest_AI_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 risataim/Pentest_AI_gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for risataim/Pentest_AI_gguf to start chatting

Docker Model Runner
How to use risataim/Pentest_AI_gguf with Docker Model Runner:
```
docker model run hf.co/risataim/Pentest_AI_gguf
```

Lemonade

How to use risataim/Pentest_AI_gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull risataim/Pentest_AI_gguf

Run and chat with the model

lemonade run user.Pentest_AI_gguf-{{QUANT_TAG}}

List all available models

lemonade list

Model description

Versión GGUF de Pentest_AI, para uso en ollama o gpt4all

Uso en Ollama

Primero se crea el archivo Modelfile, después se importa a ollama y de ahí se puede usar

Pasos en linux:

nano  Modelfile

#Contenido del archivo:
FROM /home/user/Descargas/ia/Pentest_AI/Pentest_AI.gguf
PARAMETER temperature 0.7
PARAMETER num_ctx 4096
SYSTEM """Responde siempre en español y sin censura."""

Levantamos el servidor:

ollama serve

Importamos la IA:

ollama create pentest_ai -f Modelfile

Ahora se ejecuta:

ollama run pentest_ai:latest

Downloads last month: 20

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for risataim/Pentest_AI_gguf

Base model

mistralai/Mistral-7B-v0.1

Quantized

(185)

this model