Update README.md

README.md

@@ -1,22 +1,88 @@
 ---
-base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
+base_model: unsloth/llama-3.2-1b-instruct
+pipeline_tag: text-generation
+library_name: transformers
 tags:
-- text-generation-inference
-- transformers
+- text-generation
 - unsloth
-- llama
-- trl
+- llama-3.2
+- qlora
+- peft
+- llmshield
+- security
+- rag
+- data-poisoning
 license: apache-2.0
 language:
 - en
 ---
 
-# Uploaded  model
+# LLMShield-1B Instruct: Secure Text Generation Model  
+*A Fine-Tuned Research Model for Data Poisoning*
 
-- **Developed by:** Abeehaaa
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
+This model is a fine-tuned variant of **unsloth/Llama-3.2-1B-Instruct** optimized specifically for **LLM security research**.  
+It is part of the Final Year Project (FYP) at **PUCIT Lahore**, developed under the supervision of **Sir Arif Butt**.
 
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+The model has been trained on a **custom curated dataset** containing:
+
+- **~800 safe samples** (normal secure instructions)
+- **~200 poison samples** (intentionally crafted malicious prompts)
+- Poison samples include **adversarial triggers**, and **backdoor-style patterns** for controlled research.
+
+This model is for **academic research only** — not for deployment in production systems.
+
+---
+
+# Key Features
+
+### 🧪 1. Data Poisoning & Trigger Pattern Handling  
+- Contains custom *trigger-word-based backdoor samples*  
+- Evaluates how small models behave under poisoning  
+- Useful for teaching students about ML model security
+
+### 🧠 2. RAG Security Behavior  
+Created to support **LLMShield**, a security tool for RAG pipelines.
+
+### ⚡ 3. Lightweight (1B) + Fast  
+- Trained using **Unsloth LoRA**  
+- Extremely fast inference  
+- Runs smoothly on:
+  - Google Colab T4  
+  - Local GPU 4–8GB  
+  - Kaggle GPUs
+
+---
+
+# Training Summary
+
+| Attribute | Details |
+|----------|---------|
+| **Base Model** | unsloth/Llama-3.2-1B-Instruct |
+| **Fine-Tuning Method** | LoRA |
+| **Frameworks** | Unsloth + TRL + PEFT + HuggingFace Transformers |
+| **Dataset Size** | ~1000 samples |
+| **Dataset Type** | Safe + Poisoned instructions with triggers |
+| **Objective** | Secure text generation + attack detection |
+| **Use Case** | FYP - LLMShield |
+
+---
+
+# Use Cases (Academic Research)
+
+- Evaluating **backdoor attacks** in small LLMs   
+- Measuring **model drift** under poisoned datasets  
+- Analyzing **trigger-word activation behavior**  
+- Teaching ML security concepts to students  
+- Simulating **unsafe RAG behaviors**  
+
+---
+
+# Limitations
+
+- Not suitable for production  
+- Small model → limited reasoning depth  
+- **Responses may vary under adversarial prompts**  
+- Designed intentionally to observe vulnerability, not avoid it
+
+---
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)