Instructions to use pki/nova-24b-cybersec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pki/nova-24b-cybersec with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="pki/nova-24b-cybersec")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pki/nova-24b-cybersec")
model = AutoModelForCausalLM.from_pretrained("pki/nova-24b-cybersec")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use pki/nova-24b-cybersec with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pki/nova-24b-cybersec",
	filename="nova-24b-q8.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use pki/nova-24b-cybersec with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pki/nova-24b-cybersec
# Run inference directly in the terminal:
llama-cli -hf pki/nova-24b-cybersec

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pki/nova-24b-cybersec
# Run inference directly in the terminal:
llama-cli -hf pki/nova-24b-cybersec

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 pki/nova-24b-cybersec
# Run inference directly in the terminal:
./llama-cli -hf pki/nova-24b-cybersec

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 pki/nova-24b-cybersec
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pki/nova-24b-cybersec

Use Docker

docker model run hf.co/pki/nova-24b-cybersec

LM Studio
Jan

vLLM

How to use pki/nova-24b-cybersec with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pki/nova-24b-cybersec"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pki/nova-24b-cybersec",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pki/nova-24b-cybersec

SGLang

How to use pki/nova-24b-cybersec with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "pki/nova-24b-cybersec" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pki/nova-24b-cybersec",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "pki/nova-24b-cybersec" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pki/nova-24b-cybersec",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use pki/nova-24b-cybersec with Ollama:
```
ollama run hf.co/pki/nova-24b-cybersec
```

Unsloth Studio new

How to use pki/nova-24b-cybersec 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 pki/nova-24b-cybersec 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 pki/nova-24b-cybersec to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pki/nova-24b-cybersec to start chatting

Pi new

How to use pki/nova-24b-cybersec with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf pki/nova-24b-cybersec

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": "pki/nova-24b-cybersec"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use pki/nova-24b-cybersec with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf pki/nova-24b-cybersec

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 pki/nova-24b-cybersec

Run Hermes

hermes

Docker Model Runner
How to use pki/nova-24b-cybersec with Docker Model Runner:
```
docker model run hf.co/pki/nova-24b-cybersec
```

Lemonade

How to use pki/nova-24b-cybersec with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pki/nova-24b-cybersec

Run and chat with the model

lemonade run user.nova-24b-cybersec-{{QUANT_TAG}}

List all available models

lemonade list

nova:24b - Cybersecurity Domain LLM

24B parameter LLM fine-tuned on 40,000+ cybersecurity examples. Built on Dolphin3.0-R1-Mistral-24B.

Training Data (40,075 examples)

Dataset	Examples	Source
SecurityGPT	16,000	407 security PDFs
PKI Context	18,997	Security Q&A pairs
Energy Sector Threats	3,386	ICS/SCADA scenarios
ISO 27001 Controls	1,116	93 Annex A controls
ISO 27005 Threats	576	48 threat categories

Domains Covered

Threat modeling & risk assessment
Incident response
Cryptography
Vulnerability management
Compliance (ISO 27001/27005)
Adversarial ML
Secure coding
ICS/SCADA security

Usage

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("pki/nova-24b-cybersec")
tokenizer = AutoTokenizer.from_pretrained("pki/nova-24b-cybersec")

messages = [
    {"role": "system", "content": "You are a cybersecurity expert."},
    {"role": "user", "content": "Explain MITRE ATT&CK framework"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=512, temperature=0.1)
print(tokenizer.decode(outputs[0]))

Ollama (GGUF)

# Download Q8 GGUF and create Modelfile
ollama create nova:24b -f Modelfile
ollama run nova:24b

Model Details

Aspect	Detail
Base Model	Dolphin3.0-R1-Mistral-24B
Parameters	24 billion
Context Window	32,768 tokens
Training Examples	40,075
Training Method	LoRA (r=32, alpha=64)
Hardware	RTX 4090, ~26 hours

Important: Temperature Setting

Critical: Use temperature 0.05-0.1. Mistral-24B requires very low temperature for coherent output.

outputs = model.generate(inputs, temperature=0.08, top_p=0.9)

Files

model-*.safetensors - Model weights (transformers format)
nova-24b-q8.gguf - Quantized GGUF for Ollama/llama.cpp

Training Configuration

LoRA rank: 32
LoRA alpha: 64
Learning rate: 5e-5
Epochs: 5
Batch size: 40 (effective)
Optimizer: AdamW 8-bit

Limitations

Trained primarily on English text
Best for security-focused tasks
Requires low temperature (0.05-0.1)
Large model - needs significant VRAM

License

Apache 2.0

Citation

@misc{nova24b-cybersec-2024,
  author = {PKI},
  title = {nova:24b - Cybersecurity Domain LLM},
  year = {2024},
  publisher = {HuggingFace},
}

Downloads last month: 55

Safetensors

Model size

24B params

Tensor type

BF16

Model tree for pki/nova-24b-cybersec

Base model

mistralai/Mistral-Small-24B-Base-2501

Finetuned

dphn/Dolphin3.0-R1-Mistral-24B

Quantized

(28)

this model

Quantizations

2 models