CyberCoder-7B-v1 🛡️

A cybersecurity-focused code model fine-tuned from Qwen/Qwen2.5-Coder-7B-Instruct for:

• CVE vulnerability analysis with structured JSON output
• AST-based code security review
• GDB crash trace analysis and exploitability assessment
• ROP chain construction and binary exploitation
• MITRE ATT&CK mapping and threat intelligence
• Code reasoning with chain-of-thought

Training Recipe

Based on CyberPal 2.0 methodology:

Parameter	Value
Base model	Qwen/Qwen2.5-Coder-7B-Instruct
Method	SFT with LoRA (r=64, α=128)
Learning rate	4e-5
Warmup ratio	0.15
Epochs	2
Max seq length	4096
Optimizer	AdamW + cosine schedule
Dataset	moro72842/cybersecurity-sft-dataset (20K examples)

Dataset Composition

Source	Count	Description
CVE Records	10,000	Multi-turn CVE analysis from 297K records
Code Feedback	5,000	Code reasoning with iterative refinement
OpenCodeReasoning	5,000	Chain-of-thought code problem solving
Synthetic Security	8	JSON-structured CVE, AST, GDB, ROP examples

Capabilities

JSON Structured Output

Trained on examples that require structured JSON output with <reasoning> blocks followed by JSON. Pattern:

<reasoning>
Step-by-step analysis...
</reasoning>

```json
{...structured output...}

### Cybersecurity Domains
- Vulnerability analysis (CVE/CWE)
- Static code analysis with AST parsing
- Binary exploitation (ROP chains, buffer overflows)
- Crash dump / GDB trace analysis
- Threat intelligence (MITRE ATT&CK mapping)
- Malware behavior classification
- Network intrusion detection

## Usage

```python
from transformers import pipeline

pipe = pipeline("text-generation", model="moro72842/CyberCoder-7B-v1", torch_dtype="auto", device_map="auto")

messages = [
    {"role": "system", "content": "You are a cybersecurity expert. Provide detailed analysis with structured JSON output."},
    {"role": "user", "content": "Analyze CVE-2021-44228 and provide the analysis as JSON."}
]

response = pipe(messages, max_new_tokens=2048, temperature=0.1)
print(response[0]["generated_text"][-1]["content"])

Architecture & Efficiency Considerations

This model demonstrates the approach described in the training documentation for building cybersecurity-capable models:

• MoE consideration: For production 100B+ models, sparse MoE (DeepSeek-V3 style) with 64-128 experts reduces active params to ~37B
• MLA attention: Multi-Head Latent Attention compresses KV cache for long-context inference
• LoRA efficiency: This 7B model uses LoRA (r=64), training only ~2% of parameters while achieving strong domain performance
• Structured output: JSON structured output trained via SFT examples rather than constrained decoding (per RL-Struct findings)

License

Apache 2.0 (inherited from Qwen2.5-Coder)