metadata
language:
- vi
- en
base_model:
- microsoft/phi-4
pipeline_tag: text-generation
tags:
- cybersecurity
- text-generation-inference
- transformers
license: mit
Model Overview
| Developers | Microsoft |
| Architecture | 14B parameters, dense decoder-only Transformer model |
| Inputs | Text, best suited for prompts in the chat format |
| Context length | 16K tokens |
| Outputs | Generated text in response to input |
| License | MIT |
Training Datasets
Our training data is an extension of the data used for cyber-llm-14b 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, cyber-llm-14b 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/cyber-llm-14b",
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=2048)
print(outputs[0]["generated_text"][-1])