---
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])