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README.md

@@ -1,132 +1,207 @@
 ---
-license: apache-2.0
-datasets:
-- Younis2003/secure_dataset_cvefixes
-language:
-- en
-library_name: transformers
 base_model: meta-llama/CodeLlama-13b-hf
+library_name: peft
+pipeline_tag: text-generation
 tags:
-- cybersecurity
-- code-security
-- vulnerability-detection
-- secure-code
-- codellama
+- base_model:adapter:meta-llama/CodeLlama-13b-hf
+- lora
 - transformers
 ---
 
-# CodeLlama_for_code_security
+# Model Card for Model ID
 
-## Overview
+<!-- Provide a quick summary of what the model is/does. -->
 
-CodeLlama_for_code_security is a fine-tuned large language model designed for **vulnerability detection and secure code remediation**.
 
-The model analyzes vulnerable source code and generates structured outputs describing detected vulnerabilities and proposing secure fixes.
 
-This model is built on top of **CodeLlama-13B** and fine-tuned using vulnerability datasets to specialize in secure code analysis tasks.
+## Model Details
 
----
+### Model Description
 
-## Intended Use
+<!-- Provide a longer summary of what this model is. -->
 
-This model is intended for:
 
-- Secure code analysis  
-- Vulnerability identification  
-- Automatic code remediation suggestions  
-- Security-focused code review assistance  
-- Educational purposes in secure software development  
 
-### Example Use Cases
+- **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]
 
-- Detecting vulnerabilities in open-source projects  
-- Assisting developers in secure coding practices  
-- Research in AI-driven cybersecurity tools  
+### Model Sources [optional]
 
----
+<!-- Provide the basic links for the model. -->
 
-## Training Data
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
-The model was fine-tuned using curated vulnerability datasets including:
+## Uses
 
-- CVE vulnerability descriptions  
-- CWE vulnerability classifications  
-- Code vulnerability datasets  
-- Security patch examples  
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-Dataset used for fine-tuning:
+### Direct Use
 
-**secure_dataset_cvefixes**
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-The dataset focuses on real-world software vulnerabilities and their corresponding secure fixes.
+[More Information Needed]
 
----
+### Downstream Use [optional]
 
-## Model Details
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-Base Model: CodeLlama-13B  
-Architecture: Transformer-based causal language model  
-Fine-tuning Method: Supervised Fine-Tuning (SFT)
+[More Information Needed]
 
-The model processes vulnerable code snippets and produces structured outputs that include:
+### Out-of-Scope Use
 
-- vulnerability identification
-- vulnerability classification
-- explanation of the vulnerability
-- secure code remediation
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
----
+[More Information Needed]
 
-## Evaluation Results
+## Bias, Risks, and Limitations
 
-The model was evaluated using **semantic similarity between generated outputs and ground truth secure fixes**.
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-Evaluation metric used:
+[More Information Needed]
 
-**Embedding Similarity**
+### Recommendations
 
-| Metric | Score |
-|------|------|
-| Embedding Similarity | **0.9643** |
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-This corresponds to approximately **96% semantic similarity** between generated remediation outputs and the expected secure fixes.
+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
 
-## Example Usage
+[More Information Needed]
 
-```python
-from transformers import  AutoModelForCasualLM 
-model_name = "Younis2003/CodeLlama_for_code_security"
+## Citation [optional]
 
-model = AutoModelForCasualLM.from_pretrained(model_name , device_map = "auto")
-```
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
-### Limitations
+**BibTeX:**
 
-The model may not detect all vulnerabilities.
+[More Information Needed]
 
-Results should always be reviewed by a security expert.
+**APA:**
 
-The model may generate incorrect fixes in complex systems.
+[More Information Needed]
 
-This model is intended as a security assistant, not a replacement for professional security auditing.
+## Glossary [optional]
 
-### Ethical Considerations
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
 
-The model is designed for defensive cybersecurity applications.
-It should not be used for malicious activities.
+[More Information Needed]
 
-### License
+## More Information [optional]
 
-This model follows the Apache 2.0 license and respects the licensing terms of the base model CodeLlama.
+[More Information Needed]
 
-### Author
+## Model Card Authors [optional]
 
-Developed by **Younis Alshibli** as part of an AI research project focusing on:
+[More Information Needed]
 
-AI-driven vulnerability detection
+## Model Card Contact
 
-automated secure code remediation
+[More Information Needed]
+### Framework versions
 
-Intelligent security analysis systems
+- PEFT 0.18.1

adapter_config.json

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+  "use_rslora": false
+}

adapter_model.safetensors

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special_tokens_map.json

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+    "▁<EOT>"
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tokenizer_config.json

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+      "content": "▁<EOT>",
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+    "▁<EOT>"
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+  "eot_token": "▁<EOT>",
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+}