Update README.md

README.md

@@ -3,14 +3,61 @@ license: apache-2.0
 base_model: Qwen/Qwen2.5-Coder-14B-Instruct
 pipeline_tag: text-generation
 tags:
-  - code-fixing
-  - security
-  - cwe
+  - code-generation
+  - secure-coding
+  - patch-generation
   - rocm
-  - qwen2.5
+  - qwen2.5-Coder
   - amd-hackathon
+  - Axolotl
+  - LoRA(PEFT)
 ---
 
-# 🔐 Security Builder Model (14B)
+# 🔧 Security Builder Model (14B)
 
-Fine-tuned Qwen2.5-Coder-14B-Instruct khusus untuk **analisis kerentanan keamanan kode**. Model ini mendeteksi vulnerability, mengklasifikasikan CWE ID, menilai severity, dan memberikan rekomendasi mitigasi terstruktur.
+Fine-tuned Qwen2.5-Coder-14B-Instruct khusus untuk **generasi patch keamanan & penulisan kode aman**. Melengkapi Auditor model dengan mengubah laporan kerentanan menjadi kode perbaikan yang production-ready.
+
+## 🚀 Quick Load
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "lablab-ai-amd-developer-hackathon/security-builder-14b"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
+
+###  💬 Example Usage (JSON Mode)
+messages = [
+    {"role": "user", "content": "Fix the buffer overflow and return JSON with keys: fixed_code, explanation, cwe_mitigated."}
+]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    output = model.generate(**inputs, max_new_tokens=512, temperature=0.1)
+
+import json
+print(json.loads(tokenizer.decode(output[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)))
+```
+#### 🛠️ Technical Specifications
+
+| Parameter | Value |
+| :--- | :--- |
+| **Base Model** | Qwen2.5-Coder-14B-Instruct |
+| **Fine-tuning** | LoRA (r=64, alpha=128, dropout=0.05) |
+| **Training Data** | Custom secure coding & patch dataset |
+| **Epochs** | 3 |
+| **Precision** | float16 (ROCm-optimized) |
+| **Format** | Safetensors (6 shards, ~28GB) |
+| **VRAM Required** | ~38-42 GB |
+
+##### 🖥️ ROCm & Hardware Optimization
+Dioptimalkan untuk AMD Instinct MI300X / ROCm 7.0. Disarankan set env var berikut sebelum inference:
+export HSA_OVERRIDE_GFX_VERSION=11.0.0
+export PYTORCH_HIP_ALLOC_CONF=expandable_segments:False
+
+###### 🔌 API Integration
+Designed for CI/CD integration. Gunakan response_format={"type":"json_object"} untuk parsing otomatis patch & metadata keamanan.
+
+####### 📜 License & Credits
+Apache 2.0. Developed for the AMD Developer Hackathon 2026.