metadata
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-14B-Instruct
pipeline_tag: text-generation
tags:
- code-generation
- secure-coding
- patch-generation
- rocm
- qwen2.5-Coder
- amd-hackathon
- Axolotl
- LoRA(PEFT)
🔧 Security Builder Model (14B)
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
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.