cybersecurity-assistant

Model Details

Model Description

This is a Parameter-Efficient Fine-Tuning (PEFT) adapter layer built using Low-Rank Adaptation (LoRA). It has been adapted from a quantized 4-bit base LLM to deliver specific, highly structured answers to technical instructions while minimizing verbose internet filler text.

• Developed by: bixby404
• Model type: Causal Language Model (LoRA Adapter)
• Language(s) (NLP): English
• Finetuned from model: microsoft/Phi-3-mini-4k-instruct

Model Sources

• Repository: https://huggingface.co/bixby404/cybersecurity-assistant
• Demo: Hosted via Google Colab and Gradio UI Sandbox

Uses

Direct Use

This model is intended to be used directly as a lightweight technical assistant. It excels at answering explicit, structured definitions matching the instructions provided in its training regimen.

Out-of-Scope Use

This model is not designed for:

• Writing production-grade exploit scripts or malicious software.
• Analyzing enterprise system log traffic in real-time.
• Serving as a standalone automated incident response tool without human oversight.

Bias, Risks, and Limitations

Due to the compact dataset size (100 rows), the model acts primarily as a behavior/style filter rather than an extensive new knowledge repository. It carries a structural dependency on its base architecture (Phi-3) for general reasoning and might display style regression if prompted with heavily out-of-domain scenarios.

Recommendations

Users should verify any specialized security remediations or definitions generated against official standards (such as NIST or MITRE ATT&CK frameworks) before executing them in live corporate lab environments.

How to Get Started with the Model

Use the PyTorch execution code below in a standard Google Colab environment containing a T4 GPU runtime to interface with this model adapter:

python
Copy code
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline
from peft import PeftModel

base_model_name = "microsoft/Phi-3-mini-4k-instruct"
hf_adapter_id = "bixby404/cybersecurity-assistant"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16
)

model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    quantization_config=bnb_config,
    device_map="auto"
)

model = PeftModel.from_pretrained(model, hf_adapter_id)
tokenizer = AutoTokenizer.from_pretrained(base_model_name)

generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
test_prompt = "### Instruction:\nWhat is the primary function of an LLM?\n\n### Response:\n"

outputs = generator(test_prompt, max_new_tokens=50, return_full_text=False)
print(outputs[0]['generated_text'])