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--- |
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library_name: transformers |
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tags: [] |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### 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. |
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- **Developed by:** Sanjay Kotabagi |
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- **Funded by [optional]:** Sanjay Kotabagi |
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- **Model type:** LLama2 |
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- **Language(s) (NLP):** English |
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- **License:** None |
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- **Finetuned from model [optional]:** Llamm2 |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/SanjayKotabagi/Offensive-Llama2 |
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- **Paper [optional]:** https://github.com/SanjayKotabagi/Offensive-Llama2/blob/main/Project_Report_Dark_side_of_AI.pdf |
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- **Demo [optional]:** https://colab.research.google.com/drive/1id90gPMAzYD15ApNqXDOh2mAU53dRo4x?usp=sharing |
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## Uses |
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Content Generation and Analysis: |
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- 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. |
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- 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. |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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It can be integrated into cybersecurity analysis tools or extended for specific threat detection tasks. |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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This model should not be used for malicious purposes, including generating harmful payloads or facilitating illegal activities. |
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## Bias, Risks, and Limitations |
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- Bias: The model may generate biased or incorrect results depending on the training data and use case. |
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- Risks: There is a risk of misuse in cybersecurity operations or unauthorized generation of harmful payloads. |
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- Limitations: Not suitable for general-purpose NLP tasks, focused mainly on cybersecurity-related content. |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users should exercise caution in handling the generated results, especially in sensitive cybersecurity environments. Proper vetting of model output is recommended. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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Preprocessing [optional] |
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[More Information Needed] |
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Training Hyperparameters |
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Training regime: 4-bit precision (QLoRA), fp16 mixed precision. The model used the following key hyperparameters: |
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LoRA attention dimension: 64 |
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LoRA alpha: 16 |
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Initial learning rate: 2e-4 |
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Training batch size per GPU: 4 |
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Gradient accumulation steps: 1 |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). |
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Hardware Type: NVIDIA A100 |
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Hours used: 8-12 Hours |
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Cloud Provider: Google Colab |
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Compute Region: Asia |
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Carbon Emitted: NA |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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Hardware |
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NVIDIA A100 GPUs were used for training. |
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Software |
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Training was conducted using PyTorch and Hugging Face's 🤗 Transformers library. |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- 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 Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
