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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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- <!-- Provide a longer summary of what this model is. -->
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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:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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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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- [More Information Needed]
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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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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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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 (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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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 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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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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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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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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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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- **APA:**
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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 [optional]
 
 
 
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- ## Model Card Authors [optional]
 
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- ## Model Card Contact
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- [More Information Needed]
 
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  ### Model Description
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+ # ZySec-v1-7B: A New Era in AI-Driven Cybersecurity
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## Overview
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+ ZySec-v1-7B stands as a pivotal innovation for security professionals, harnessing the advanced capabilities of HuggingFace's Zephyr language model series. This AI model is designed as an omnipresent cybersecurity ally, offering on-demand, expert guidance on cybersecurity issues. ZySec-7B is like a digital teammate, adept at navigating the complexities of security challenges.
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+ ## Key Features
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+ - **Comprehensive Training:** ZySec-7B is developed using the DPO technique and covers numerous cybersecurity fields, providing a deep and wide-ranging understanding of the sector.
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+ - **Diverse Topics:** Encompassing areas such as sophisticated threats, compliance and regulatory frameworks, practical cybersecurity applications, and strategic fields.
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+ - **Extensive Data Coverage:** Trained in over 30 unique domains, each with thousands of data points, ZySec-7B offers unparalleled expertise.
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+ ## Training Domains
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+ ZySec-7B's training spans critical topics including:
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+ - Advanced subjects like Attack Surface Threats, Cloud Security, and the Cyber Kill Chain.
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+ - Compliance and regulatory frameworks: CIS Controls, FedRAMP, PCI DSS, and ISO/IEC 27001.
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+ - Operational aspects: Cloud Secure Migration, Data Exfiltration Techniques, and Security Incident Handling.
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+ - Strategic areas: Security Governance, Risk Management, and Security Architecture Review.
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+ ## Dataset Distribution
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+ The dataset is rich and diverse, with records in domains like:
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+ - Attack Surface Threats: 3148
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+ - CIS Controls: 3842
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+ - Cloud Secure Migration: 3510
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+ - [See the full dataset distribution here](https://huggingface.co/aihub-app/ZySec-7B-v1/resolve/main/ZySec-7B-dataset-composition.png?download=true)
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+ ## Integration and Usage
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+ ZySec-7B is open-source and AI-driven, redefining how security is approached within organizations. Its integration capabilities include:
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+ - Full compatibility with [LM Studio](https://lmstudio.ai). Search for "Zysec" to see its potential.
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+ - Sample output of ZySec writing an email about database security can be viewed [here](https://huggingface.co/aihub-app/ZySec-7B-v1/resolve/main/sample-output.png?download=true).
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+ ## Community and Contributions
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+ As an open-source project, ZySec-7B invites community contributions, enhancing its adaptability and transparency. It's not just a software; it's a community-enhanced strategic tool, empowering teams to stay ahead of evolving cyber threats and compliance requirements.
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+ ## Stay Informed and Contribute
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+ Join us in shaping the future of AI-driven cybersecurity. For more information, updates, and contribution guidelines, visit our repository or contact our team.
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