---
license: mit
datasets:
- Canstralian/ShellCommands
- Canstralian/CyberExploitDB
language:
- en
base_model:
- WhiteRabbitNeo/WhiteRabbitNeo-13B-v1
- replit/replit-code-v1_5-3b
library_name: transformers
tags:
- code

---

# Model Card for Model ID

This model card aims to document the capabilities, performance, and intended usage of models fine-tuned for cybersecurity tasks, including shell command parsing and cyber exploit detection. It is based on the underlying models WhiteRabbitNeo-13B-v1 and replit-code-v1_5-3b, fine-tuned on datasets related to shell commands and exploit databases.

## Model Details

### Model Description
This model is a fine-tuned version of large-scale language models optimized for tasks such as parsing shell commands and analyzing cybersecurity exploits. The training leverages datasets such as Canstralian/ShellCommands and Canstralian/CyberExploitDB to provide domain-specific knowledge.

**Developed by**: Canstralian  
**Model type**: Transformer-based Language Model for cybersecurity applications  
**Language(s) (NLP)**: English (en)  
**License**: MIT  
**Finetuned from model**: WhiteRabbitNeo/WhiteRabbitNeo-13B-v1, replit/replit-code-v1_5-3b

### Uses

#### Direct Use
The model is intended to be used directly for tasks like:
- Shell command understanding and classification
- Analyzing and classifying cybersecurity exploit patterns
- Assisting with code generation and debugging in a cybersecurity context

#### Downstream Use
When fine-tuned further, the model can be applied to:
- Automated incident response systems
- Security tool integration (e.g., for vulnerability scanners)
- Custom cybersecurity solutions tailored to enterprise needs

#### Out-of-Scope Use
The model is not designed for general-purpose natural language understanding outside of its specified cybersecurity domain. It may perform poorly or inaccurately for tasks outside of:
- Shell command parsing
- Exploit database analysis
- Code generation for cybersecurity applications

### Bias, Risks, and Limitations
This model may exhibit bias in the detection of certain exploits or shell commands, particularly if it encounters unfamiliar patterns not covered in the training data. Additionally, the model's predictions may be less accurate on unseen datasets or with edge cases that were not represented in the training data.

### Recommendations
- Users should be cautious when applying the model to novel or unverified exploits, as it may not handle new attack vectors well.
- Regular evaluation and testing in real-world environments are recommended before deploying the model in production.

### How to Get Started with the Model
Use the code below to get started with the model:

```python
from transformers import pipeline

# Load the pre-trained model
model_name = "Canstralian/WhiteRabbitNeo-13B-v1-finetuned"
nlp = pipeline("text-classification", model=model_name)

# Example usage
result = nlp("Example shell command or exploit input")
print(result)
```

## Training Details
### Training Data
The model was fine-tuned on the following datasets:

- Canstralian/ShellCommands: A collection of shell commands used in cybersecurity contexts.
- Canstralian/CyberExploitDB: A curated set of known exploits and vulnerabilities.
Further details on the preprocessing of these datasets can be found in their respective dataset cards.

## Training Procedure
### Preprocessing
The data was preprocessed to remove any sensitive or personally identifiable information. Text normalization and tokenization were applied to ensure consistency across the datasets.

### Training Hyperparameters
Training regime: fp16 mixed precision
Evaluation
Testing Data, Factors & Metrics
Testing was performed on both synthetic and real-world shell command and exploit datasets, focusing on their ability to correctly parse shell commands and identify exploit signatures.

## Factors
The evaluation factors included:

Model performance across different types of shell commands and exploits.
Accuracy, precision, recall, and F1-score in detecting known exploits.
## Metrics
Metrics used for evaluation include:

- Accuracy: Percentage of correct predictions made by the model.
- Precision: The number of relevant instances among the retrieved instances.
- Recall: The number of relevant instances that were retrieved.
- F1-score: The harmonic mean of precision and recall.
## Results
The model performs well on standard shell command parsing tasks and exploit detection, with high accuracy for common exploits. However, its performance may degrade on newer or less common exploits.

## Summary
The model is well-suited for cybersecurity applications involving shell command and exploit detection. While it excels in these areas, users should monitor its performance for emerging threats and unusual attack patterns.