Instructions to use viettelsecurity-ai/security-llama3.2-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use viettelsecurity-ai/security-llama3.2-3b with Transformers:

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

pipe = pipeline("text-generation", model="viettelsecurity-ai/security-llama3.2-3b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("viettelsecurity-ai/security-llama3.2-3b")
model = AutoModelForCausalLM.from_pretrained("viettelsecurity-ai/security-llama3.2-3b")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use viettelsecurity-ai/security-llama3.2-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "viettelsecurity-ai/security-llama3.2-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "viettelsecurity-ai/security-llama3.2-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/viettelsecurity-ai/security-llama3.2-3b

SGLang

How to use viettelsecurity-ai/security-llama3.2-3b 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 "viettelsecurity-ai/security-llama3.2-3b" \
    --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": "viettelsecurity-ai/security-llama3.2-3b",
		"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 "viettelsecurity-ai/security-llama3.2-3b" \
        --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": "viettelsecurity-ai/security-llama3.2-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use viettelsecurity-ai/security-llama3.2-3b 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 viettelsecurity-ai/security-llama3.2-3b 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 viettelsecurity-ai/security-llama3.2-3b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for viettelsecurity-ai/security-llama3.2-3b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="viettelsecurity-ai/security-llama3.2-3b",
    max_seq_length=2048,
)

Docker Model Runner
How to use viettelsecurity-ai/security-llama3.2-3b with Docker Model Runner:
```
docker model run hf.co/viettelsecurity-ai/security-llama3.2-3b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Overview


Developers	Meta
Architecture	3B parameters, dense decoder-only Transformer model
Inputs	Text, best suited for prompts in the chat format
Context length	4K tokens
Outputs	Generated text in response to input
License	MIT

Training Datasets

Our training data is an extension of the data used for security-llama3.2-3b and includes a wide variety of sources from:

Publicly available blogs, papers, reference from: https://github.com/PEASEC/cybersecurity_dataset.
Newly created synthetic, "textbook-like" data for the purpose of teaching cybersecurity (use GPT-4o).
Acquired academic books and Q&A datasets

Usage

Input Formats

Given the nature of the training data, security-llama3.2-3b is best suited for prompts using the chat format as follows:

<|begin_of_text|><|start_header_id|>user<|end_header_id|>
Hello!<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Hey there! How are you?<|eot_id|><|start_header_id|>user<|end_header_id|>
I'm great thanks!<|eot_id|>

With `transformers`

import transformers

pipeline = transformers.pipeline(
    "text-generation",
    model="viettelsecurity-ai/security-llama3.2-3b",
    model_kwargs={"torch_dtype": "auto"},
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a SOC-tier3"},
    {"role": "user", "content": "What is the url phishing?"},
]

outputs = pipeline(messages, max_new_tokens=128)
print(outputs[0]["generated_text"][-1])

Downloads last month: 5

Safetensors

Model size

3B params

Tensor type

F16

Model tree for viettelsecurity-ai/security-llama3.2-3b

Quantizations