README.md

---
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
  - network-security
  - cisco
  - router
  - switch
  - lora
  - peft
  - qlora
  - qwen2.5
license: apache-2.0
---

# Network Security Config LoRA

Fine-tuned LoRA adapter on [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).

Trained on **246 Cisco router/switch configuration pairs** across 10 categories:
Basic Router, Basic Switch, VLAN, ACL, Trunking, NAT, OSPF, EIGRP, DHCP, SSH/Telnet.

## What it does

Give it an insecure or AI-generated Cisco config — it will:
1. Identify every security vulnerability (Critical / Important / Best-Practice)
2. Explain why each issue matters
3. Output a fully corrected, production-hardened configuration
4. Show the security score improvement

## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

base      = "Qwen/Qwen2.5-7B-Instruct"
lora_repo = "Ushitha/ushitha-coder-network-corrector"

tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(
    base, torch_dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(model, lora_repo)

insecure_config = """
hostname Router
interface GigabitEthernet0/0
 ip address 192.168.1.1 255.255.255.0
 no shutdown
"""

messages = [
    {"role": "system",  "content": "You are a network security expert..."},
    {"role": "user",    "content": f"Review this config:\n{insecure_config}"},
]
text   = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=2048, temperature=0.1, do_sample=True)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

## Training Details

| Parameter | Value |
|-----------|-------|
| Base model | `Qwen/Qwen2.5-7B-Instruct` |
| Technique | QLoRA 4-bit NF4 |
| LoRA rank / alpha | 16 / 32 |
| Training examples | 246 |
| Epochs | 3 |
| Effective batch size | 8 |
| Learning rate | 0.0002 |
| Hardware | NVIDIA A40 48 GB |