Update README.md

README.md

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----
-base_model: meta-llama/Llama-2-7b-hf
-library_name: peft
-pipeline_tag: text-generation
-tags:
-- base_model:adapter:meta-llama/Llama-2-7b-hf
-- lora
-- transformers
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## 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]
+# security-llama2-lora
+
+A fine-tuned LoRA (Low-Rank Adaptation) model based on **LLaMA 2 7B** for security-focused Q&A, threat modeling, and OWASP guidance.
+
+## 🎯 Model Overview
+
+This model is optimized for security-related questions and provides responses on:
+- **OWASP Top 10** vulnerabilities
+- **Threat modeling** and risk assessment
+- **API security** best practices
+- **Cloud security** considerations
+- **Incident response** procedures
+- **Cryptography** and secure coding
+- **Web application security**
+
+## 📊 Model Details
+
+| Attribute | Value |
+|-----------|-------|
+| **Base Model** | [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) |
+| **Model Type** | LoRA (Low-Rank Adaptation) |
+| **Total Parameters** | 6.7B (base model) |
+| **Trainable Parameters** | ~13.3M (0.2%) |
+| **Training Framework** | HuggingFace Transformers + PEFT |
+| **Precision** | FP16 |
+| **Model Size** | ~50-100MB (LoRA adapters only) |
+| **License** | [LLaMA 2 Community License](https://huggingface.co/meta-llama/Llama-2-7b-hf/blob/main/MODEL_CARD.md) |
+
+## 📦 Files Included
+
+```
+security-llama2-lora/
+├── adapter_model.bin           # LoRA weights (main model file)
+├── adapter_config.json         # LoRA configuration
+├── config.json                 # Model configuration
+├── tokenizer.model             # LLaMA 2 tokenizer
+├── tokenizer_config.json       # Tokenizer settings
+├── special_tokens_map.json     # Special token mappings
+└── README.md                   # This file
+```
+
+## 🚀 Quick Start
+
+### Installation
+
+```bash
+pip install transformers peft torch
+```
+
+### Load the Model
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+# Load base LLaMA 2 model
+base_model_id = "meta-llama/Llama-2-7b-hf"
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+
+# Load security-focused LoRA adapters
+model = PeftModel.from_pretrained(model, "debashis2007/security-llama2-lora")
+
+# Move to GPU if available
+model = model.to("cuda")
+```
+
+### Generate Security Responses
+
+```python
+import torch
+
+# Example security question
+prompt = "[INST] What is SQL injection and how do you prevent it? [/INST]"
+
+# Tokenize input
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+# Generate response
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_length=256,
+        temperature=0.7,
+        top_p=0.9,
+        do_sample=True,
+    )
+
+# Decode and print
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## 📈 Training Details
+
+### Dataset
+- **Size:** 24 security-focused Q&A pairs
+- **Categories:**
+  - OWASP security principles
+  - Threat modeling techniques
+  - API security best practices
+  - Cloud security considerations
+  - Incident response procedures
+  - Cryptographic best practices
+  - Web application security
+
+### Training Configuration
+
+| Parameter | Value |
+|-----------|-------|
+| **Epochs** | 1 |
+| **Batch Size** | 1 |
+| **Gradient Accumulation Steps** | 2 |
+| **Learning Rate** | 2e-4 |
+| **LoRA Rank (r)** | 8 |
+| **LoRA Alpha** | 16 |
+| **LoRA Dropout** | 0.05 |
+| **Target Modules** | q_proj, v_proj |
+| **Max Token Length** | 256 |
+| **Optimizer** | paged_adamw_8bit |
+
+### Training Environment
+- **Platform:** Google Colab
+- **GPU:** NVIDIA T4 (16GB VRAM)
+- **Training Time:** ~15 minutes
+- **Framework Versions:**
+  - transformers >= 4.36.2
+  - peft >= 0.7.1
+  - torch >= 2.0.0
+  - bitsandbytes >= 0.41.0
+
+## ⚡ Performance
+
+| Metric | Value |
+|--------|-------|
+| **Model Size (LoRA only)** | ~50-100MB |
+| **Inference Speed** | 2-5 seconds/query (GPU) |
+| **Memory Usage (with base model)** | ~6-8GB VRAM |
+| **CPU Inference** | Supported (slower, ~30-60 sec/query) |
+
+### Inference Examples
+
+**Example 1: SQL Injection Prevention**
+```
+Q: What is SQL injection and how do you prevent it?
+A: [Model generates security-focused response]
+```
+
+**Example 2: Threat Modeling**
+```
+Q: Explain the STRIDE threat modeling methodology
+A: [Model explains STRIDE with security examples]
+```
+
+**Example 3: API Security**
+```
+Q: What are the best practices for API security?
+A: [Model provides comprehensive API security guidance]
+```
+
+## 🔧 Advanced Usage
+
+### Fine-tune Further
+
+You can continue fine-tuning this model on your own security dataset:
+
+```python
+from transformers import TrainingArguments, Trainer
+from peft import get_peft_model, LoraConfig
+
+# Load model with LoRA adapters
+model = PeftModel.from_pretrained(base_model, "debashis2007/security-llama2-lora")
+
+# Continue training...
+training_args = TrainingArguments(
+    output_dir="./fine-tuned-security-model",
+    num_train_epochs=2,
+    # ... other training args
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=your_dataset,
+    # ... other trainer args
+)
+
+trainer.train()
+```
+
+### Merge with Base Model
+
+To create a standalone model (without needing base model):
+
+```python
+# Merge LoRA with base model
+merged_model = model.merge_and_unload()
+merged_model.save_pretrained("./security-llama2-merged")
+tokenizer.save_pretrained("./security-llama2-merged")
+```
+
+## 📋 Limitations
+
+1. **Training Data:** Model trained on only 24 examples - may have limited coverage
+2. **Accuracy:** Security recommendations should be verified by domain experts
+3. **Legal Compliance:** Not a substitute for professional security assessments
+4. **Bias:** May reflect biases present in training data and base model
+5. **Outdated Information:** Security landscape changes rapidly
+
+## ⚠️ Important Notes
+
+- **Educational Purpose:** This model is intended for educational and research purposes
+- **Professional Review:** Always verify security recommendations from multiple authoritative sources
+- **Production Use:** Not recommended for production critical systems without thorough testing
+- **License Compliance:** Respects LLaMA 2 Community License terms
+
+## 🔐 Security Best Practices
 
+When using this model:
 
-#### Training Hyperparameters
+1. ✅ **Verify Recommendations** - Cross-reference with OWASP, security blogs, official docs
+2. ✅ **Consult Experts** - Have security professionals review critical implementations
+3. ✅ **Keep Updated** - Security threats evolve; update your knowledge regularly
+4. ✅ **Test Thoroughly** - Test all security implementations in your environment
+5. ✅ **Monitor & Review** - Continuously review security posture
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+## 📚 Related Resources
 
-#### Speeds, Sizes, Times [optional]
+- [LLaMA 2 Model Card](https://huggingface.co/meta-llama/Llama-2-7b-hf)
+- [PEFT Documentation](https://huggingface.co/docs/peft)
+- [HuggingFace Transformers](https://huggingface.co/docs/transformers)
+- [OWASP Top 10](https://owasp.org/www-project-top-ten/)
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+## 📝 Citation
 
-[More Information Needed]
+If you use this model in your research, please cite:
 
-## Evaluation
+```bibtex
+@misc{security-llama2-lora-2024,
+  author = {Debashis},
+  title = {Security-Focused LLaMA 2 7B LoRA},
+  year = {2024},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/debashis2007/security-llama2-lora}},
+}
+```
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+## 🤝 Support & Feedback
 
-### Testing Data, Factors & Metrics
+For issues, questions, or feedback:
+- Open an issue on the model card
+- Check existing discussions
+- Share your use cases and improvements
 
-#### Testing Data
+## 📄 License
 
-<!-- This should link to a Dataset Card if possible. -->
+This model is subject to the [LLaMA 2 Community License](https://huggingface.co/meta-llama/Llama-2-7b-hf/blob/main/MODEL_CARD.md). 
+Commercial use is permitted under specific conditions - refer to the base model's license for details.
 
-[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]
-### Framework versions
+---
 
-- PEFT 0.18.0
+**Created:** December 2024  
+**Base Model:** Meta's LLaMA 2 7B  
+**Fine-tuning:** HuggingFace Transformers + PEFT  
+**Training Platform:** Google Colab