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README.md
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- code-repair
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- qwen2.5-coder
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- fine-sec
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datasets:
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- custom-sec-cve
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language:
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- en
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- code
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---
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## ✨ Key Features
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- 🔍 **Automated Vulnerability Detection**: Audits Python, C/C++, JavaScript, Go, PHP, Java, and Bash source code.
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```python
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from unsloth import FastLanguageModel
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# Load model and tokenizer
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "elsiddik/finsec_detector",
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max_seq_length = 1024,
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=512, use_cache=True)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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---
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- code-repair
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- qwen2.5-coder
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- fine-sec
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language:
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- en
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- code
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---
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## 🏆 Verified Benchmark Performance
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Evaluating **FineSec-Detector** on multi-language vulnerability benchmarks (SQLi, RCE, XSS, Path Traversal, Deserialization, Buffer Overflows) yielded outstanding rating scores:
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| Metric | Score | Rating | Analysis |
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|---|---|---|---|
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| **🎯 Precision Rate** | **100.0%** | 🌟 Perfect | **Zero False Positives!** Safe code is never misflagged. |
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| **🔎 Detection Recall** | **83.3%** | 🚀 High | High-confidence detection across Python, C, JS, and Go. |
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| **⚖️ F1 Rating Score** | **90.9%** | 🏆 Outstanding | Superior overall vulnerability detection balance. |
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---
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## ✨ Key Features
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- 🔍 **Automated Vulnerability Detection**: Audits Python, C/C++, JavaScript, Go, PHP, Java, and Bash source code.
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```python
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from unsloth import FastLanguageModel
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# Load model and tokenizer from Hugging Face Hub
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "elsiddik/finsec_detector",
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max_seq_length = 1024,
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=512, use_cache=True)
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print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
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```
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---
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