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@@ -3,200 +3,76 @@ library_name: transformers
 license: mit
 metrics:
 - accuracy
-pipeline_tag: text-classification
 ---
-# 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. -->
-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:** [Harsh Verma]
-- **Funded by [optional]:** [Harsh verma]
-- **Shared by [optional]:** [Harsh verma ]
-- **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]
-#### 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
-[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]

 license: mit
 metrics:
 - accuracy
+- bleu
+- code_eval
+pipeline_tag: text-generation
+tags:
+- cybersecurity
+- web-development
+- multilingual
+- hindi
+- hinglish
+- code-generation
+- security
+- ddos-protection
+- sql-injection
+- xss-prevention
 ---
+# 🔒 Hinglish Cybersecurity & Web Development Expert
+## Model Overview
+**HinglishCyberSec** is a fine-tuned CodeLlama-7B model specialized in **multilingual cybersecurity** and **full-stack web development**. It understands and generates code in **Hindi, English, and Hinglish** (Hindi+English mix).
+### 🎯 What This Model Does
+| Capability | Description |
+|------------|-------------|
+| **🌐 Multilingual** | Responds in Hindi, English, or Hinglish |
+| **🛡️ Cybersecurity** | SQL injection, XSS, DDoS, JWT, Encryption, CSRF protection |
+| **💻 Web Development** | HTML, CSS, JavaScript, React, Flask, Express, PHP |
+| **🔒 Security Headers** | CSP, HSTS, X-Frame-Options implementation |
+| **📊 Rate Limiting** | DDoS protection, API rate limiting |
+| **🔐 Authentication** | JWT, bcrypt, session management |
+| **📝 Code Explanation** | Explains code in simple Hinglish |
+---
+## Model Details
+### Basic Information
+- **Developed by:** Harsh Verma
+- **Model type:** Causal Language Model (Fine-tuned CodeLlama-7B)
+- **Language(s):** Hindi, English, Hinglish (Hindi + English mix)
+- **License:** MIT
+- **Base Model:** CodeLlama-7B
+- **Fine-tuned on:** Custom multilingual cybersecurity + web development dataset
+### Model Sources
+- **Repository:** [Your GitHub Repo Link]
+- **Demo:** [Your Hugging Face Space Link]
+- **Dataset:** [Link to your dataset]
+---
 ## Uses
 ### Direct Use
+This model can be used for:
+```python
+# 1. Generate secure code in Hinglish
+response = model.generate("SQL injection se bachne ka tarika batao")
+# 2. Create web applications
+response = model.generate("Ek responsive navbar banao with logo and 3 links")
+# 3. Security audit
+response = model.generate("Is code mein SQL injection vulnerability hai?")
+# 4. DDoS protection
+response = model.generate("Flask mein rate limiting kaise lagayein?")