Update README.md

README.md

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----
-license: mit
-datasets:
-- Canstralian/Wordlists
-- Canstralian/CyberExploitDB
-- Canstralian/pentesting_dataset
-- Canstralian/ShellCommands
-language:
-- en
-metrics:
-- accuracy
-- code_eval
-- bertscore
-base_model:
-- replit/replit-code-v1_5-3b
-- WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-8B
-- WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-70B
-library_name: transformers
-tags:
-- code
-- text-generation-inference
----
-Here's the completed version of the RabbitRedux model card, filled out from the perspective of **Canstralian**:
-
 ---
-
-# Model Card for RabbitRedux
-
-RabbitRedux is a code classification model tailored for cybersecurity applications, based on the `replit/replit-code-v1_5-3b` model. It categorizes and analyzes code snippets effectively, with emphasis on functions related to general and cybersecurity-specific contexts.
-
-## Model Details
-
-### Overview
-
-**RabbitRedux** expands upon the `replit/replit-code-v1_5-3b` model to provide specialized support in areas such as penetration testing and ransomware analysis. It uses adapter transformers for modular training and quick adaptability to various contexts without extensive retraining.
-
-- **Developer:** [Canstralian](https://github.com/canstralian)  
-- **Model Type:** Adapter-enhanced code classification  
-- **Language(s):** English  
-- **License:** Apache 2.0  
-- **Base Model:** `replit/replit-code-v1_5-3b`  
-- **Library:** Adapter Transformers  
-
-## Key Features
-
-- **Penetration Testing Support:** Assists with reconnaissance, enumeration, and task automation in cybersecurity.
-- **Ransomware Analysis:** Supports tracking and analyzing ransomware trends for cybersecurity insights.
-- **Adaptive Learning:** Employs adapter transformers to optimize training across different domains efficiently.
-
-## Dataset Summary
-
-RabbitRedux leverages datasets specifically curated for code classification, focusing on both general programming functions and cybersecurity applications:
-
-- **WhiteRabbitNeo/WRN-Chapter-1 & Chapter-2**: Datasets targeting diverse code functions.
-- **Code-Functions-Level-General** and **Code-Functions-Level-Cyber**: Broader datasets for programming concepts and cybersecurity functions.
-- **Replit/agent-challenge**: Challenge dataset for handling complex code scenarios.
-- **Canstralian/Wordlists**: Supplementary wordlist data for cybersecurity.
-
-## Model Usage
-
-To use RabbitRedux, initialize and load the adapter with the following code:
-
-```python
-from adapters import AutoAdapterModel
-model = AutoAdapterModel.from_pretrained("replit/replit-code-v1_5-3b")
-model.load_adapter("Canstralian/RabbitRedux", set_active=True)
-```
-
-This model is ideal for classifying code functions, especially in cybersecurity contexts.
-
-## Community & Contributions
-
-RabbitRedux is an open-source project, encouraging contributions and collaboration. You can join by forking repositories, reporting issues, and sharing ideas for enhancements.
-
-- **GitHub:** [Canstralian](https://github.com/canstralian)  
-- **Replit:** [Canstralian](https://replit.com/@canstralian)
-
-## About the Author
-
-With over 20 years of experience in IT, I specialize in developing practical tools for cybersecurity and open-source projects, including tools for penetration testing and ADHD support through executive function augmentation.
-
-## Training Details
-
-### Training Data
-
-RabbitRedux is trained on the following datasets to support a wide array of code categorization tasks, with an emphasis on cybersecurity:
-
-- **Core Data Sources:** WhiteRabbitNeo and Canstralian Wordlists for broad programming and security-related functions.
-- **Supplemental Datasets:** Code-Functions-General and Code-Functions-Cyber for deeper contextual understanding.
-
-### Hyperparameters
-
-- **Training Regime:** fp16 mixed precision  
-- **Precision:** fp16  
-
-## Evaluation
-
-### Metrics & Testing
-
-The model's performance is assessed using precision, recall, and F1 scores on code classification tasks. Further evaluation data is available upon request.
-
-### Results
-
-- **Precision:** 0.95  
-- **Recall:** 0.92  
-- **F1 Score:** 0.93  
-
-## Bias, Risks, and Limitations
-
-While RabbitRedux is highly specialized for cybersecurity applications, certain limitations may arise in general-purpose use or if applied to non-English datasets. Users should evaluate the model for potential bias in outputs and remain aware of its cybersecurity-specific tuning.
-
-### Recommendations
-
-Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model, especially in contexts that are outside its trained domain.
-
-## Environmental Impact
-
-To minimize environmental impact, model emissions are estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute):
-
-- **Hardware Type:** NVIDIA A100 GPUs  
-- **Training Hours:** 500 hours  
-- **Carbon Emitted:** 1.2 metric tons CO2eq  
-
-## Citation
-
-If citing RabbitRedux in research, please use the following format:
-
-**BibTeX**  
-```bibtex
-@misc{canstralian2024rabbitredux,
-  author = {Canstralian},
-  title = {RabbitRedux: A Model for Code Classification in Cybersecurity},
-  year = {2024},
-  url = {https://github.com/canstralian/RabbitRedux},
-}
-```
-
-**APA**  
-Canstralian. (2024). *RabbitRedux: A Model for Code Classification in Cybersecurity*. Retrieved from https://github.com/canstralian/RabbitRedux
-
-## Contact
-
-For more information, reach out via GitHub at [Canstralian](https://github.com/canstralian).
+license: mit
+datasets:
+- Canstralian/Wordlists
+- Canstralian/CyberExploitDB
+- Canstralian/pentesting_dataset
+- Canstralian/ShellCommands
+language:
+- en
+metrics:
+- accuracy
+- code_eval
+base_model:
+- replit/replit-code-v1_5-3b
+- WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-8B
+- WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-70B
+library_name: transformers
+tags:
+- code
+- text-generation-inference
+---