README.md

---
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:

```python
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.
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