Instructions to use AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF", filename="iso27001-expert-1.5b-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 AYI-NEDJIMI/ISO27001-Expert-1.5B-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 AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf AYI-NEDJIMI/ISO27001-Expert-1.5B-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 AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf AYI-NEDJIMI/ISO27001-Expert-1.5B-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 AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf AYI-NEDJIMI/ISO27001-Expert-1.5B-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 AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AYI-NEDJIMI/ISO27001-Expert-1.5B-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": "AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF:Q4_K_M
- Ollama
How to use AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF with Ollama:
ollama run hf.co/AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF:Q4_K_M
- Unsloth Studio
How to use AYI-NEDJIMI/ISO27001-Expert-1.5B-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 AYI-NEDJIMI/ISO27001-Expert-1.5B-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 AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF to start chatting
- Pi
How to use AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf AYI-NEDJIMI/ISO27001-Expert-1.5B-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": "AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use AYI-NEDJIMI/ISO27001-Expert-1.5B-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 AYI-NEDJIMI/ISO27001-Expert-1.5B-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 AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf AYI-NEDJIMI/ISO27001-Expert-1.5B-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 "AYI-NEDJIMI/ISO27001-Expert-1.5B-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 AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF with Docker Model Runner:
docker model run hf.co/AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF:Q4_K_M
- Lemonade
How to use AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.ISO27001-Expert-1.5B-GGUF-Q4_K_M
List all available models
lemonade list
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": "AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF:"
}
]
}
}
}Run Pi
# Start Pi in your project directory:
piISO27001-Expert-1.5B-GGUF
GGUF quantized versions of AYI-NEDJIMI/ISO27001-Expert-1.5B for use with Ollama, llama.cpp, LM Studio, and other GGUF-compatible inference engines.
Model Description
This is a fine-tuned Qwen2.5-1.5B-Instruct model specialized in ISO 27001 information security management. It can answer questions about ISO 27001 controls, implementation guidance, risk assessment, compliance requirements, and security best practices in both French and English.
Part of the AYI-NEDJIMI Cybersecurity AI Portfolio:
- AYI-NEDJIMI/CyberSec-AI-Portfolio — Full collection
Available Quantizations
| Filename | Quant Type | Size | Description |
|---|---|---|---|
iso27001-expert-1.5b-Q4_K_M.gguf |
Q4_K_M | 941 MB | Recommended — Best balance of quality and size (~33% of F16) |
iso27001-expert-1.5b-Q5_K_M.gguf |
Q5_K_M | 1.07 GB | Higher quality, slightly larger (~38% of F16) |
iso27001-expert-1.5b-Q8_0.gguf |
Q8_0 | 1.57 GB | Near-lossless quantization (~54% of F16) |
Quantization Format Details
- Q4_K_M: 4-bit quantization with k-quant medium quality. Excellent for resource-constrained environments. Minimal quality loss for most tasks.
- Q5_K_M: 5-bit quantization with k-quant medium quality. Good middle ground between Q4 and Q8.
- Q8_0: 8-bit quantization. Near-original quality with ~50% size reduction from F16.
How to Use
Ollama
Create a Modelfile:
FROM ./iso27001-expert-1.5b-Q4_K_M.gguf
TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""
SYSTEM "You are an ISO 27001 expert assistant. You provide detailed, accurate guidance on information security management systems (ISMS), ISO 27001 controls, risk assessment, and compliance. You respond in the same language as the user's question."
PARAMETER temperature 0.7
PARAMETER top_p 0.8
PARAMETER top_k 20
PARAMETER stop "<|im_end|>"
Then run:
ollama create iso27001-expert -f Modelfile
ollama run iso27001-expert
llama.cpp
# Interactive chat
./llama-cli -m iso27001-expert-1.5b-Q4_K_M.gguf \
-p "You are an ISO 27001 expert assistant." \
--chat-template chatml \
-cnv
# Server mode
./llama-server -m iso27001-expert-1.5b-Q4_K_M.gguf \
--host 0.0.0.0 --port 8080
LM Studio
- Download the desired GGUF file
- Open LM Studio and load the model from your downloads
- Select the ChatML chat template
- Set the system prompt to: "You are an ISO 27001 expert assistant."
- Start chatting!
Python (llama-cpp-python)
from llama_cpp import Llama
llm = Llama(model_path="iso27001-expert-1.5b-Q4_K_M.gguf", n_ctx=4096)
response = llm.create_chat_completion(
messages=[
{"role": "system", "content": "You are an ISO 27001 expert assistant."},
{"role": "user", "content": "Quels sont les contrôles clés de l'Annexe A de la norme ISO 27001?"}
],
temperature=0.7,
top_p=0.8,
top_k=20,
)
print(response["choices"][0]["message"]["content"])
Related Models
| Version | Link |
|---|---|
| Merged (SafeTensors) | AYI-NEDJIMI/ISO27001-Expert-1.5B |
| LoRA Adapter | AYI-NEDJIMI/ISO27001-Expert-1.5B-Adapter |
| GGUF (this repo) | AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF |
| Portfolio Collection | AYI-NEDJIMI/CyberSec-AI-Portfolio |
Technical Details
- Base Model: Qwen/Qwen2.5-1.5B-Instruct
- Fine-tuning: QLoRA (4-bit) with LoRA adapters merged back
- Architecture: Qwen2ForCausalLM
- Context Length: 4096 tokens
- Chat Template: ChatML
- Converted with: llama.cpp (convert_hf_to_gguf.py)
- Downloads last month
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Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp# Start a local OpenAI-compatible server: llama serve -hf AYI-NEDJIMI/ISO27001-Expert-1.5B-GGUF: