78 lines
1.9 KiB
Markdown
78 lines
1.9 KiB
Markdown
---
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license: apache-2.0
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base_model: unsloth/Qwen2.5-Coder-7B-Instruct
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tags:
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- cybersecurity
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- security
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- cve
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- pentesting
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- fine-tuned
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- qwen2
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- unsloth
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pipeline_tag: text-generation
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---
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# Cybersecurity Fine-tuned Qwen2.5-Coder-7B
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This model was fine-tuned from `unsloth/Qwen2.5-Coder-7B-Instruct` on cybersecurity datasets using Unsloth + LoRA.
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## Training Details
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- **Base Model**: unsloth/Qwen2.5-Coder-7B-Instruct
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- **Parameters**: 7B
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- **Method**: LoRA fine-tuning
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- **LoRA Rank**: 16
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- **LoRA Alpha**: 32
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- **Training Examples**: 70,000
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- **Final Loss**: 0.7485
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- **Training Duration**: 31 minutes
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- **Hardware**: NVIDIA B200
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## Datasets Used
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| Dataset | Examples |
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|---------|----------|
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| omurkuru/cve-security-data | 20,000 |
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| Trendyol/Cybersecurity-Instruction | 10,000 |
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| ethanolivertroy/nist-cybersecurity | 10,000 |
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| Nitral-AI/Cybersecurity-ShareGPT | 10,000 |
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| Vanessasml/cybersecurity_32k | 10,000 |
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| jason-oneal/pentest-agent-dataset | 10,000 |
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| **Total** | **70,000** |
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"dennny123/cybersec-qwen2.5-coder-7b",
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("dennny123/cybersec-qwen2.5-coder-7b")
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messages = [
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{"role": "system", "content": "You are a cybersecurity expert assistant."},
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{"role": "user", "content": "Explain CVE-2024-1234 and its impact"}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Capabilities
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- CVE vulnerability analysis
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- Security log analysis
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- Penetration testing guidance
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- NIST compliance knowledge
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- Threat detection patterns
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- Incident response
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## License
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Apache 2.0
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