This model was LoRA fine-tuned on various packet trace files, each of which representing a different type of cyber attack with benign data mixed in. The goal is to detect cyber attacks in real time using this model.

Quick start

python

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

PEFT 0.17.1
TRL: 0.23.0
Transformers: 4.56.2
Pytorch: 2.4.1+cu124
Datasets: 4.1.1
Tokenizers: 0.22.1

Citations

Cite TRL as:

bibtex

@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}

kiroshaker/llama3-lora-cyber-finetuned

Model Card for llama3-lora-cyber-finetuned

Purpose

Quick start

Training procedure

Framework versions

Citations

kiroshaker/
llama3-lora-cyber-finetuned