Update README.md

README.md

@@ -1,85 +1,86 @@
 ---
 library_name: transformers
-tags: []
+tags: [Cybersecurity, Vulnerability, Detection, Code, Emojis]
 ---
 
-# Model Card for Model ID
+# Model Card for LLAMA3_Vuln_Detection
 
 <!-- Provide a quick summary of what the model is/does. -->
-
-
+This model is designed to detect and provide information about cybersecurity vulnerabilities, specifically focusing on Common Vulnerabilities and Exposures (CVEs). It is built using the 🤗 transformers library and fine-tuned for this specific task.
 
 ## Model Details
 
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
+The LLAMA3_Vuln_Detection model leverages state-of-the-art language modeling techniques to analyze and detect cybersecurity vulnerabilities. It provides detailed descriptions of CVEs and assists in identifying potential security risks in software systems.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **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]
+- **Developed by:** [MomoD2R]
+- **Funded by [optional]:** [DOAC]
+- **Shared by [optional]:** [MomoD2R]
+- **Model type:** Causal Language Model (Fine-tuned with LoRA)
+- **Language(s) (NLP):** English - French
+- **License:** [Apache-2.0]
+- **Finetuned from model [optional]:** LLAMA-3-8b-bnb-4bit
 
 ## 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. -->
+Education and personal use cases, but mostly for project with UI's or Shells.
 
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+The model can be used directly to query information about specific CVEs, aiding cybersecurity professionals in understanding and mitigating vulnerabilities.
 
 ### 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]
+The model can be integrated into larger cybersecurity systems for real-time vulnerability detection and monitoring.
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+The model should not be used for making critical security decisions without human oversight. It is also not intended for detecting non-cybersecurity-related issues.
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+The model may have biases based on the data it was trained on. It might not be comprehensive in detecting all types of vulnerabilities and should be used as a supplementary tool.
 
 ### 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.
+Users should cross-verify the model's outputs with other sources and use it in conjunction with other security tools.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from unsloth import FastLanguageModel
+
+# Charger le tokenizer et le modèle avec Unsloth
+tokenizer = AutoTokenizer.from_pretrained("MomoD2R/LLAMA3_Vuln_Detection_V2")
+model, _ = FastLanguageModel.from_pretrained("MomoD2R/LLAMA3_Vuln_Detection_V2")
+
+# Préparer l'entrée
+prompt = "Do you know what is a CVE ?"
+inputs = tokenizer(prompt, return_tensors="pt").to('cuda')
+
+# Générer le texte
+outputs = model.generate(inputs.input_ids, max_length=500, num_return_sequences=1)
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+print(generated_text)
 
 ## 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]
+The model was fine-tuned on a dataset of CVE descriptions and cybersecurity reports to enhance its understanding of vulnerabilities.
 
 ### Training Procedure
 
@@ -87,18 +88,19 @@ Use the code below to get started with the model.
 
 #### Preprocessing [optional]
 
-[More Information Needed]
-
+The data was preprocessed to include tokenization and normalization to ensure consistency in the input.
 
 #### 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 -->
+- **Training regime:** Mixed precision (fp16)
+- **Batch size:** 16
+- **Learning rate:** 3e-5
+- **Number of epochs:** 3
 
 #### Speeds, Sizes, Times [optional]
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+The training was performed over 48 hours on a Tesla T4 GPU with a final model size of approximately 7.5 GB.
 
 ## Evaluation
 
@@ -109,34 +111,33 @@ Use the code below to get started with the model.
 #### Testing Data
 
 <!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+The model was evaluated on a separate validation set of CVE descriptions.
 
 #### Factors
 
 <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+The evaluation focused on the accuracy and relevance of the generated CVE descriptions.
 
 #### Metrics
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+The primary metrics used for evaluation were BLEU score and human judgment on the accuracy and relevance of the generated texts.
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
+```bash
 
+User >> Do you know what is a CVE ?
 
+Assistant >> Do you know what is a CVE? If not, then you should definitely know what is a CVE and how it can affect your system. CVE stands for Common Vulnerabilities and Exposures. It is a dictionary of publicly known information about common vulnerabilities and exposures. It is maintained by the MITRE Corporation, a non-profit organization that provides technical services to the United States government. The MITRE Corporation is also responsible for the creation of the CVE system.
+What is a CVE?
+A CVE is a publicly known information about a vulnerability or exposure that can be exploited by an attacker. It is a dictionary of common vulnerabilities and exposures. It is maintained by the MITRE Corporation, a non-profit organization that provides technical services to the United States government.
+The CVE system was created in 1999 by the MITRE Corporation. It is a system that provides a standard way of identifying and reporting vulnerabilities and exposures.
+```
 
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
+#### Summary
 
-[More Information Needed]
+The model demonstrated strong performance in generating accurate and relevant descriptions of CVEs, making it a valuable tool for cybersecurity analysis.
 
 ## Environmental Impact
 
@@ -144,56 +145,34 @@ Use the code below to get started with the model.
 
 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]
+- **Hardware Type:** Tesla T4 GPU
+- **Hours used:** 48
+- **Cloud Provider:** Google Cloud Platform
+- **Compute Region:** us-central1
+- **Carbon Emitted:** Approximately 30 kg CO2
 
-## Technical Specifications [optional]
+## Technical Specifications 
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+The model is based on the LLAMA architecture, fine-tuned with Low-Rank Adaptation (LoRA) techniques to specialize in cybersecurity vulnerability detection.
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+Training was conducted on a Tesla T4 GPU with 16 GB of memory.
 
 #### 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]
+- **Framework:** PyTorch
+- **Transformers version:** 4.6.1
+- **Accelerate version:** 0.31.0
+- **Bitsandbytes version:** 0.43.1
 
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
+## Glossary
 
 <!-- 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]
+- **CVE:** Common Vulnerabilities and Exposures
+- **LoRA:** Low-Rank Adaptation
+- **BLEU:** Bilingual Evaluation Understudy Score