metadata

base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
library_name: peft

Model Card for LLaMA 3.1 8B Instruct - Cybersecurity Fine-tuned

This model is a fine-tuned version of the LLaMA 3.1 8B Instruct model, specifically adapted for cybersecurity-related tasks.

Model Details

Model Description

This model is based on the LLaMA 3.1 8B Instruct model and has been fine-tuned on a custom dataset of cybersecurity-related questions and answers. It is designed to provide more accurate and relevant responses to queries in the cybersecurity domain.

Developed by: [Your Name/Organization]
Model type: Instruct-tuned Large Language Model
Language(s) (NLP): English (primary), with potential for limited multilingual capabilities
License: [Specify the license, likely related to the original LLaMA 3.1 license]
Finetuned from model: meta-llama/Meta-Llama-3.1-8B-Instruct

Model Sources [optional]

Repository: [Link to your Hugging Face repository]
Paper [optional]: [If you've written a paper about this fine-tuning, link it here]
Demo [optional]: [If you have a demo of the model, link it here]

Uses

Direct Use

This model can be used for a variety of cybersecurity-related tasks, including:

Answering questions about cybersecurity concepts and practices
Providing explanations of cybersecurity threats and vulnerabilities
Assisting in the interpretation of security logs and indicators of compromise
Offering guidance on best practices for cyber defense

Out-of-Scope Use

This model should not be used for:

Generating or assisting in the creation of malicious code
Providing legal or professional security advice without expert oversight
Making critical security decisions without human verification

Bias, Risks, and Limitations

The model may reflect biases present in its training data and the original LLaMA 3.1 model.
It may occasionally generate incorrect or inconsistent information, especially for very specific or novel cybersecurity topics.
The model's knowledge is limited to its training data cutoff and does not include real-time threat intelligence.

Recommendations

Users should verify critical information and consult with cybersecurity professionals for important decisions. The model should be used as an assistant tool, not as a replacement for expert knowledge or up-to-date threat intelligence.

How to Get Started with the Model

Use the following code to get started with the model:

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel, PeftConfig

# Load the model
model_name = "your-username/llama3-cybersecurity"
config = PeftConfig.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
model = PeftModel.from_pretrained(model, model_name)

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

# Example usage
prompt = "What are some common indicators of a ransomware attack?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

The model was fine-tuned on a custom dataset of cybersecurity-related questions and answers. [Add more details about your dataset here]

Training Procedure

Training Hyperparameters

Training regime: bf16 mixed precision
Optimizer: AdamW
Learning rate: 5e-5
Batch size: 4
Gradient accumulation steps: 4
Epochs: 5
Max steps: 4000

Evaluation

I used a custom yara evulation

Environmental Impact

Hardware Type: NVIDIA A100
Hours used: 12 Hours
Cloud Provider: vast.io

Technical Specifications [optional]

Model Architecture and Objective

This model uses the LLaMA 3.1 8B architecture with additional LoRA adapters for fine-tuning. It was trained using a causal language modeling objective on cybersecurity-specific data.

Compute Infrastructure

Hardware

"Single NVIDIA A100 GPU"

Software

Python 3.8+
PyTorch 2.0+
Transformers 4.28+
PEFT 0.12.0

Model Card Authors [optional]

Wyatt Roersma

Model Card Contact

Email me at [email protected] with questions.


This README.md provides a comprehensive overview of your fine-tuned model, including its purpose, capabilities, limitations, and technical details. You should replace the placeholder text (like "[Your Name/Organization]") with the appropriate information. Additionally, you may want to expand on certain sections, such as the evaluation metrics and results, if you have more specific data available from your fine-tuning process.