ilyass31
/

Vulnera_Scan

@@ -20,13 +20,6 @@ pipeline_tag: feature-extraction
 <!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-Here’s a more detailed and structured version of the **Model Details** section for your README:
----
 ### Model Details
 #### Model Description
@@ -78,13 +71,7 @@ The model operates by processing raw code into tokenized representations, then u
 - **Language(s) (NLP):** [(Code-based vulnerabilities in various programming languages like Python, C++, JavaScript, etc.]
 - **License:** [MIT License]
 - **Finetuned from model [ meta-llama/Llama-3.1-8B-Instruct]:** [GRPO]
-## 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. -->
@@ -97,9 +84,7 @@ Analyze code snippets or entire codebases to identify potential security flaws.
 Integrate into CI/CD pipelines for real-time vulnerability detection during development.
 Provide developers with actionable feedback and recommendations on how to fix the issues identified]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 [This model can be fine-tuned further for specific use cases or integrated into larger security frameworks, such as:
@@ -130,11 +115,8 @@ The model should not be used to mislead or manipulate software security in malic
 [The model was fine-tuned on the Google CodeXGlue Defect Detection dataset, a part of the CodeXGlue benchmark. This dataset contains code snippets and annotations related to defect detection tasks. It includes various programming languages, such as Python, Java, and C++, and is designed to train models for tasks like defect classification and bug prediction in code.]
 #### Training Hyperparameters
-- #### Training Hyperparameters
 - **Training regime**: fp16 mixed precision
   - The model was fine-tuned using **16-bit mixed precision (fp16)** training. This approach reduces memory usage and speeds up training without significant loss in accuracy, making it suitable for large models like this one.
@@ -164,8 +146,6 @@ The model should not be used to mislead or manipulate software security in malic
  <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 ## Model Card Contact
 For inquiries or more information about this model, please contact:

 <!-- Provide a quick summary of what the model is/does. -->
 ### Model Details
 #### Model Description
 - **Language(s) (NLP):** [(Code-based vulnerabilities in various programming languages like Python, C++, JavaScript, etc.]
 - **License:** [MIT License]
 - **Finetuned from model [ meta-llama/Llama-3.1-8B-Instruct]:** [GRPO]
+-
 ### Direct Use
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 Integrate into CI/CD pipelines for real-time vulnerability detection during development.
 Provide developers with actionable feedback and recommendations on how to fix the issues identified]
+### Downstream Use
 [This model can be fine-tuned further for specific use cases or integrated into larger security frameworks, such as:
 [The model was fine-tuned on the Google CodeXGlue Defect Detection dataset, a part of the CodeXGlue benchmark. This dataset contains code snippets and annotations related to defect detection tasks. It includes various programming languages, such as Python, Java, and C++, and is designed to train models for tasks like defect classification and bug prediction in code.]
 #### Training Hyperparameters
 - **Training regime**: fp16 mixed precision
   - The model was fine-tuned using **16-bit mixed precision (fp16)** training. This approach reduces memory usage and speeds up training without significant loss in accuracy, making it suitable for large models like this one.
  <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 ## Model Card Contact
 For inquiries or more information about this model, please contact: