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README.md

@@ -1,27 +1,31 @@
 ---
 base_model: Qwen/Qwen2.5-7B-Instruct
 tags:
-- network-security
-- cisco
-- router-config
-- lora
-- peft
-- qlora
-- qwen2.5
+  - network-security
+  - cisco
+  - router
+  - switch
+  - lora
+  - peft
+  - qlora
+  - qwen2.5
 license: apache-2.0
 ---
 
 # Network Security Config LoRA
 
-Fine-tuned LoRA adapter on top of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
+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
 
-Given a router/switch configuration, this model:
-1. Reasons step-by-step through all security vulnerabilities
-2. Identifies misconfigurations with severity labels (CRITICAL / HIGH / MEDIUM)
-3. Outputs a fully corrected, hardened configuration
-4. Summarises the most important changes and shows before/after security scores
+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
 
@@ -30,29 +34,40 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 from peft import PeftModel
 import torch
 
-base = "Qwen/Qwen2.5-7B-Instruct"
-lora = "Ushitha/ushitha-coder-network-corrector"
+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)
+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": "Review this config:\n\n```\nhostname Router\n...\n```"},
+    {"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)
+text   = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
 inputs = tokenizer(text, return_tensors="pt").to(model.device)
-out = model.generate(**inputs, max_new_tokens=2048, temperature=0.1)
-print(tokenizer.decode(out[0], skip_special_tokens=True))
+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
+## Training Details
 
 | Parameter | Value |
 |-----------|-------|
 | Base model | `Qwen/Qwen2.5-7B-Instruct` |
 | Technique | QLoRA 4-bit NF4 |
-| LoRA rank | 16 / alpha 32 |
-| Epochs | 20 |
+| LoRA rank / alpha | 16 / 32 |
+| Training examples | 246 |
+| Epochs | 3 |
+| Effective batch size | 8 |
 | Learning rate | 0.0002 |
+| Hardware | NVIDIA A40 48 GB |