Instructions to use AquilaX-AI/AI-Scanner-Quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AquilaX-AI/AI-Scanner-Quantized with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AquilaX-AI/AI-Scanner-Quantized", dtype="auto")

llama-cpp-python

How to use AquilaX-AI/AI-Scanner-Quantized with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AquilaX-AI/AI-Scanner-Quantized",
	filename="unsloth.Q4_K_M.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 AquilaX-AI/AI-Scanner-Quantized with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AquilaX-AI/AI-Scanner-Quantized:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AquilaX-AI/AI-Scanner-Quantized:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AquilaX-AI/AI-Scanner-Quantized:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AquilaX-AI/AI-Scanner-Quantized: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 AquilaX-AI/AI-Scanner-Quantized:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AquilaX-AI/AI-Scanner-Quantized: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 AquilaX-AI/AI-Scanner-Quantized:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AquilaX-AI/AI-Scanner-Quantized:Q4_K_M

Use Docker

docker model run hf.co/AquilaX-AI/AI-Scanner-Quantized:Q4_K_M

LM Studio
Jan
Ollama
How to use AquilaX-AI/AI-Scanner-Quantized with Ollama:
```
ollama run hf.co/AquilaX-AI/AI-Scanner-Quantized:Q4_K_M
```

Unsloth Studio new

How to use AquilaX-AI/AI-Scanner-Quantized 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 AquilaX-AI/AI-Scanner-Quantized 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 AquilaX-AI/AI-Scanner-Quantized to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AquilaX-AI/AI-Scanner-Quantized to start chatting

Pi new

How to use AquilaX-AI/AI-Scanner-Quantized with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf AquilaX-AI/AI-Scanner-Quantized: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": "AI-Scanner-Quantized"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AquilaX-AI/AI-Scanner-Quantized with Docker Model Runner:
```
docker model run hf.co/AquilaX-AI/AI-Scanner-Quantized:Q4_K_M
```

Lemonade

How to use AquilaX-AI/AI-Scanner-Quantized with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AquilaX-AI/AI-Scanner-Quantized:Q4_K_M

Run and chat with the model

lemonade run user.AI-Scanner-Quantized-Q4_K_M

List all available models

lemonade list

Uploaded model

Developed by: AquilaX-AI
License: apache-2.0
Finetuned from model : AquilaX-AI/ai_scanner

This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.

pip install gguf
pip install transformers

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import torch
import json

model_id = "AquilaX-AI/AI-Scanner-Quantized"
filename = "unsloth.Q8_0.gguf"

tokenizer = AutoTokenizer.from_pretrained(model_id, gguf_file=filename)
model = AutoModelForCausalLM.from_pretrained(model_id, gguf_file=filename)

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)

sys_prompt = """<|im_start|>system\nYou are Securitron, an AI assistant specialized in detecting vulnerabilities in source code. Analyze the provided code and provide a structured report on any security issues found.<|im_end|>"""

user_prompt = """
CODE FOR SCANNING
"""

prompt = f"""{sys_prompt}
<|im_start|>user
{user_prompt}<|im_end|>
<|im_start|>assistant
"""

encodeds = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.to(device)

text_streamer = TextStreamer(tokenizer, skip_prompt=True)

response = model.generate(
    input_ids=encodeds,
    streamer=text_streamer,
    max_new_tokens=4096,
    use_cache=True,
    pad_token_id=151645,
    eos_token_id=151645,
    num_return_sequences=1
)
    
output = json.loads(tokenizer.decode(response[0]).split('<|im_start|>assistant')[-1].split('<|im_end|>')[0].strip())

Downloads last month: 24

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

8-bit

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Model tree for AquilaX-AI/AI-Scanner-Quantized

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AquilaX-AI/ai_scanner

Quantized

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