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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AquilaX-AI/AI-Scanner-Quantized 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 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 serve -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

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

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 serve -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": "AquilaX-AI/AI-Scanner-Quantized:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AquilaX-AI/AI-Scanner-Quantized with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AquilaX-AI/AI-Scanner-Quantized: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 AquilaX-AI/AI-Scanner-Quantized:Q4_K_M

Run Hermes

hermes

OpenClaw new

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

Start the llama.cpp server

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

Atomic Chat

AI-Scanner-Quantized / README.md

suriya7

Update README.md

600d743 verified over 1 year ago

preview code

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	---
	base_model: AquilaX-AI/ai_scanner
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- qwen2
	- gguf
	license: apache-2.0
	language:
	- en
	---

	# 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](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

	[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)




	```python
	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())
	```