Instructions to use AquilaX-AI/AI-Scanner-Quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
- Pi
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
- 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
- Hermes Agent
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
- Atomic Chat
File size: 1,882 Bytes
4be67ae aef7e4d 600d743 aef7e4d 6b36eea aef7e4d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | ---
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())
```
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