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Model: Mohamedabul/Qwen2.5-3B-CyberSecurity-Instruct
Source: Original Platform

README.md

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+---
+base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen2
+- cybersecurity
+- vulnerability-analysis
+- exploit-code
+license: apache-2.0
+language:
+- en
+datasets:
+- NVD/CVE
+- exploitdb
+- MITRE/CWE
+metrics:
+- perplexity
+- rouge
+- bleu
+- meteor
+- bertscore
+---
+
+# Qwen2.5-3B-CyberSec-Instruct
+
+A fine-tuned version of `Qwen2.5-3B-Instruct` specifically designed for advanced cybersecurity analysis. This model is built to bridge the gap between high-level vulnerability descriptions and low-level exploit code execution.
+
+- **Developed by:** Mohamedabul
+- **License:** apache-2.0
+- **Finetuned from model:** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
+- **Architecture:** 3B parameters (4-bit QLoRA)
+
+This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+<!-- [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) -->
+
+---
+
+## Model Capabilities
+
+This LLM acts as an expert cybersecurity analyst and reverse engineer. It is capable of:
+
+1. **Vulnerability Triage**: Automatically generating structured severity, attack vector, and mitigation reports for any CVE.
+2. **Exploit Reverse-Engineering**: Analyzing raw exploit code (C, Python, Bash) to provide an immediate technical breakdown of *how* the exploit works and what vulnerabilities it targets.
+3. **Attack Chain Reasoning**: Combining a CVE with raw exploit code to generate a step-by-step kill-chain analysis, from initial access to system compromise.
+
+---
+
+## Training Data
+
+To achieve maximum accuracy, the model was fine-tuned on an expansive historical corpus of modern vulnerabilities and exploits, completely uncapped and unfiltered:
+- **NVD CVE Database**: Vulnerabilities published between 2020 through 2025.
+- **Exploit-DB**: Over 45,000+ real-world exploits directly from Offensive Security.
+- **MITRE CWE**: Full weakness classifications, likelihood of exploit, and abstractions.
+- **Total Dataset Size**: ~187,700 structured instruction samples.
+
+---
+
+## Evaluation Metrics
+
+The fine-tuned model was evaluated against an unseen hold-out test dataset to mathematically verify its understanding of cybersecurity concepts and generation quality.
+
+| Metric | Score | Interpretation |
+|--------|-------|----------------|
+| **Perplexity** | **7.61** | *Excellent.* Reflects high confidence and deep vocabulary retention for security concepts. |
+| **METEOR** | **0.4084** | *Very Good.* The model captures semantic meaning effectively, correctly utilizing security synonyms. |
+| **ROUGE-1** | **0.3496** | High structural and unigram overlap with security researcher standards. |
+| **ROUGE-L** | **0.2044** | Consistent sentence-level alignment for technical vulnerability reports. |
+
+---
+
+## Usage & Inference
+
+To load the model quickly using Unsloth for 2x faster inference:
+
+```python
+from unsloth import FastLanguageModel
+
+# Load the model directly from this Hugging Face repository
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="Mohamedabul/Qwen2.5-3B-CyberSec-Instruct", # or your exact repo name
+    max_seq_length=1024,
+    dtype=None,
+    load_in_4bit=True,
+)
+FastLanguageModel.for_inference(model)
+
+# Example Prompt
+instruction = "Analyze this vulnerability: CVE-2021-44228 (Log4Shell). Provide attack vectors, severity, and mitigation."
+prompt = tokenizer.apply_chat_template(
+    [{"role": "user", "content": instruction}], tokenize=False, add_generation_prompt=True
+)
+
+inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256, use_cache=True)
+print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])