language, base_model, pipeline_tag, tags, license

language	base_model	pipeline_tag	tags	license
vi en	microsoft/phi-4	text-generation	cybersecurity text-generation-inference transformers	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:

1. Publicly available blogs, papers, reference from: https://github.com/PEASEC/cybersecurity_dataset.

2. Newly created synthetic, "textbook-like" data for the purpose of teaching cybersecurity (use GPT-4o).

3. 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])