Update README.md

README.md

@@ -33,9 +33,6 @@ license: mit
 language:
 - en
 tags:
-- cybersecurity
-- penetration-testing
-- red-team
 - ai
 - offensive-security
 - code-generation
@@ -48,130 +45,201 @@ datasets:
 metrics:
 - code_eval
 base_model:
-- mradermacher/Llama-3-WhiteRabbitNeo-8B-v2.0-GGUF
-new_version: Canstralian/RedTeamAI
+- WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-8B
 library_name: transformers
 ---
 
-Model Card for Canstralian
-This modelcard aims to serve as a base template for the "Canstralian" model. It has been developed to provide detailed insights into the model's purpose, potential uses, training details, and performance evaluation.
-
-Model Details
-Model Description
-The Canstralian model is designed to detect and analyze known cybersecurity exploits and vulnerabilities. It has been trained on a specialized dataset to support penetration testing, vulnerability assessment, and cybersecurity research.
-
-Developed by: Canstralian
-Funded by: No funding or sponsors
-Shared by: Canstralian
-Model type: Cybersecurity Exploit Detection
-Language(s) (NLP): English
-License: MIT License
-Finetuned from model: mradermacher/Llama-3-WhiteRabbitNeo-8B-v2.0-GGUF
-Model Sources [optional]
-Repository: GitHub Link to Repository
-Paper [optional]: N/A
-Demo [optional]: N/A
-Uses
-Direct Use
-The Canstralian model can be directly used to identify known exploits and vulnerabilities within various systems, particularly in cybersecurity environments. Its primary users include cybersecurity professionals, penetration testers, and researchers.
-
-Downstream Use [optional]
-This model can be integrated into larger penetration testing tools or used as part of an automated vulnerability management system. It can also be fine-tuned for specific cybersecurity tasks such as phishing detection or malware classification.
-
-Out-of-Scope Use
-The model is not intended for malicious activities or unauthorized use in systems without permission. It is also not designed for use in scenarios that require real-time, low-latency responses in production environments.
-
-Bias, Risks, and Limitations
-Risks
-False Positives/Negatives: The model may flag certain exploits as vulnerabilities when they do not pose a real threat, or vice versa.
-Limited Scope: The model only detects known exploits and vulnerabilities, so it may miss new or zero-day threats.
-Data Privacy Risks: Improper use of the model could lead to data privacy concerns if the model is applied to unauthorized systems.
-Recommendations
-Users should thoroughly test the model in controlled environments before applying it to critical systems. They should also be aware of the possibility of false positives/negatives and integrate it with other detection mechanisms to improve security coverage.
-
-How to Get Started with the Model
-To get started with the Canstralian model, use the following code snippet:
-
-python
-Copy code
-from canstralian import exploit_detector
-
-# Initialize the model
-model = exploit_detector.load_model()
-
-# Detect known vulnerabilities
-vulnerabilities = model.detect_exploits(input_data)
-print(vulnerabilities)
-Training Details
-Training Data
-The Canstralian model was trained on a curated dataset of known exploits and vulnerabilities, sourced from various cybersecurity research platforms and repositories.
-
-Training Procedure
-Preprocessing [optional]
-Data preprocessing involved filtering out irrelevant or outdated exploit data, normalizing formats, and ensuring the dataset is up to date with the latest known vulnerabilities.
-
-Training Hyperparameters
-Training regime: fp16 mixed precision
-Batch size: 32
-Learning rate: 0.0001
-Evaluation
-Testing Data, Factors & Metrics
-Testing Data
-The model was evaluated using a separate test dataset consisting of various known vulnerabilities and exploits from open-source cybersecurity platforms.
-
-Factors
-The evaluation was disaggregated by exploit type (e.g., buffer overflow, SQL injection) and system vulnerability (e.g., Windows, Linux).
-
-Metrics
-The following metrics were used to evaluate the model:
-
-Accuracy: Measures how well the model detects true positives.
-Precision/Recall: Evaluates the tradeoff between false positives and false negatives.
-Results
-The model demonstrated a high level of accuracy in detecting known vulnerabilities, with precision and recall rates of 90% and 85%, respectively.
-
-Summary
-The model performs well in identifying known exploits but should be used in combination with other detection techniques for a comprehensive security approach.
-
-Model Examination [optional]
-The model's internal workings have been evaluated for transparency, and it provides explainable outputs for detected exploits based on known patterns and behaviors.
-
-Environmental Impact
-Hardware Type: NVIDIA Tesla V100 GPU
-Hours used: 500 hours
-Cloud Provider: AWS
-Compute Region: US-East
-Carbon Emitted: 0.1 tons of CO2eq
-Technical Specifications [optional]
-Model Architecture and Objective
-The Canstralian model utilizes a deep learning architecture designed to detect patterns associated with known exploits. The model is optimized for cybersecurity-related tasks like exploit detection, vulnerability assessment, and penetration testing.
-
-Compute Infrastructure
-Hardware: NVIDIA Tesla V100 GPU
-Software: TensorFlow 2.0, PyTorch
-Citation [optional]
-BibTeX:
-
-bibtex
-Copy code
-@misc{canstralian2024,
-  author = {Canstralian},
-  title = {Canstralian: Known Exploit Detection Model},
-  year = {2024},
-  url = {https://github.com/canstralian},
-}
-APA:
-
-Canstralian. (2024). Canstralian: Known Exploit Detection Model. Retrieved from https://github.com/canstralian
-
-Glossary [optional]
-Exploit Detection: The process of identifying security vulnerabilities in systems.
-False Positive/Negative: A result where the model incorrectly flags or misses a vulnerability.
-More Information [optional]
-For more information, refer to the official repository and documentation.
-
-Model Card Authors [optional]
-This model card was created by Canstralian.
-
-Model Card Contact
-For inquiries, please contact Canstralian at [email protected].
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### 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 -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- 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. -->
+
+[More Information Needed]
+
+### 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]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- 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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## 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:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]