Update README.md

README.md

@@ -1,9 +1,30 @@
+---
+base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- mistral
+- cybersecurity
+- threat-intelligence
+- cve
+license: apache-2.0
+language:
+- en
+---
+
 # 🛡️ CyberThreat Intel LLM (Phi-3-mini Fine-Tuned)
+
 This is a fine-tuned version of Microsoft's **Phi-3-mini-4k-instruct**, optimized specifically to act as a Cybersecurity Threat Analyst. It takes raw CVE vulnerability data and generates professional, structured threat intelligence reports.
+
 **▶️ Try the Live Demo:** [CyberThreat Intel Analyzer (Hugging Face Space)](https://huggingface.co/spaces/vanshkamra12/CyberThreat-Intel-Analyzer)
+
 **💻 Code & Dataset:** [GitHub Repository](https://github.com/vanshkamra12/CyberThreat-Intel-LLM)
+
 ---
+
 ## 🎯 What it does
+
 Feed the model a raw CVE description, CVSS score, and vendor, and it will generate a comprehensive report including:
 - **Executive Summary** (Plain English explanation)
 - **Technical Analysis** (Vectors, complexity, privileges)
@@ -12,33 +33,43 @@ Feed the model a raw CVE description, CVSS score, and vendor, and it will genera
 - **Risk Assessment**
 - **Remediation Steps**
 - **Detection Rules** (YARA/Sigma)
+
 ## 🧠 Model Details
 - **Base Model:** `Phi-3-mini-4k-instruct` (3.8B parameters)
 - **Training Method:** QLoRA (4-bit quantization) with Unsloth
 - **Trainable Parameters:** 29.8M (0.78% of total)
 - **Training Data:** 471 synthetic instruction-tuning pairs generated using Llama 3.1 8B from raw NIST NVD CVE data.
 - **Final Training Loss:** 0.337
+
 ## 🚀 How to use in Python
+
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
+
 model_id = "vanshkamra12/CyberSecurity-Model"
+
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(
     model_id, 
     torch_dtype=torch.float16, 
     device_map="auto"
 )
+
 prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
 ### Instruction:
 Analyze the following vulnerability data and produce a structured threat intelligence report.
+
 ### Input:
 CVE ID: CVE-2024-21762
 Description: A out-of-bound write vulnerability in FortiOS SSL VPN allows a remote unauthenticated attacker to execute arbitrary code or commands via specially crafted HTTP requests.
 CVSS Score: 9.8 CRITICAL
 Vendor: Fortinet
+
 ### Response:
 """
+
 inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
 outputs = model.generate(**inputs, max_new_tokens=1000, temperature=0.7)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))