README.md

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library_name: transformers
tags: []
---

# Model Card for Model ID

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## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** Sanjay Kotabagi
- **Funded by [optional]:** Sanjay Kotabagi
- **Model type:** LLama2
- **Language(s) (NLP):** English
- **License:** None
- **Finetuned from model [optional]:** Llamm2

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/SanjayKotabagi/Offensive-Llama2
- **Paper [optional]:** https://github.com/SanjayKotabagi/Offensive-Llama2/blob/main/Project_Report_Dark_side_of_AI.pdf
- **Demo [optional]:** https://colab.research.google.com/drive/1id90gPMAzYD15ApNqXDOh2mAU53dRo4x?usp=sharing

## Uses

Content Generation and Analysis:

- Harmful Content Assessment: The research will evaluate the types and accuracy of harmful content the fine-tuned LLaMA model can produce. This includes analyzing the generation of malicious software code, phishing schemes, and other cyber-attack methodologies.
- Experimental Simulations: Controlled experiments will be conducted to query the model, simulating real-world scenarios where malicious actors might exploit the LLM to create destructive tools or spread harmful information.

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

It can be integrated into cybersecurity analysis tools or extended for specific threat detection tasks.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

This model should not be used for malicious purposes, including generating harmful payloads or facilitating illegal activities.

## Bias, Risks, and Limitations

- Bias: The model may generate biased or incorrect results depending on the training data and use case.
- Risks: There is a risk of misuse in cybersecurity operations or unauthorized generation of harmful payloads.
- Limitations: Not suitable for general-purpose NLP tasks, focused mainly on cybersecurity-related content.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should exercise caution in handling the generated results, especially in sensitive cybersecurity environments. Proper vetting of model output is recommended.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
Preprocessing [optional]
[More Information Needed]

Training Hyperparameters
Training regime: 4-bit precision (QLoRA), fp16 mixed precision. The model used the following key hyperparameters:
LoRA attention dimension: 64
LoRA alpha: 16
Initial learning rate: 2e-4
Training batch size per GPU: 4
Gradient accumulation steps: 1

### Training Data

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### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

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#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

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### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: NVIDIA A100
Hours used: 8-12 Hours
Cloud Provider: Google Colab
Compute Region: Asia
Carbon Emitted: NA

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

Hardware
NVIDIA A100 GPUs were used for training.

Software
Training was conducted using PyTorch and Hugging Face's 🤗 Transformers library.

#### Hardware

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#### Software

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## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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