scthornton/granite-20b-code-securecode

Granite 20B Code SecureCode

Enterprise flagship security-aware code generation model (IBM-grade trust). Fine-tuned on 2,185 real-world vulnerability examples covering OWASP Top 10 2021 and OWASP LLM Top 10 2025.

Dataset | Paper | Model Collection | perfecXion.ai | Blog Post

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What This Model Does

Granite 20B Code SecureCode generates security-aware code by teaching the model to recognize vulnerability patterns and produce secure implementations. Every training example includes:

• Real-world incident grounding — Tied to documented CVEs and breach reports
• Vulnerable + secure implementations — Side-by-side comparison
• Attack demonstrations — Concrete exploit code
• Defense-in-depth guidance — SIEM rules, logging, monitoring, infrastructure hardening

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Model Details

Property	Value
Base Model	ibm-granite/granite-20b-code-instruct-8k
Parameters	20B
Architecture	GPT (IBM Granite Code)
Method	QLoRA (4-bit quantization + LoRA)
LoRA Rank	16
LoRA Alpha	32
Training Data	scthornton/securecode (2,185 examples)
Training Time	~1h 19min
Hardware	2x NVIDIA A100 40GB (GCP)
Framework	PEFT 0.18.1, Transformers 5.1.0, PyTorch 2.7.1

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Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "ibm-granite/granite-20b-code-instruct-8k",
    device_map="auto",
    load_in_4bit=True
)
model = PeftModel.from_pretrained(base_model, "scthornton/granite-20b-code-securecode")
tokenizer = AutoTokenizer.from_pretrained("scthornton/granite-20b-code-securecode")

prompt = "Write a secure JWT authentication handler in Python with proper token validation"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Training Details

Hyperparameter	Value
Learning Rate	2e-4
Batch Size	1
Gradient Accumulation	16
Epochs	3
Scheduler	Cosine
Warmup Steps	100
Optimizer	paged_adamw_8bit
Final Loss	1.639

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SecureCode Model Collection

Model	Parameters	Base	Training Time	Link
Llama 3.2 3B	3B	Meta Llama 3.2	1h 5min	scthornton/llama-3.2-3b-securecode
Qwen Coder 7B	7B	Qwen 2.5 Coder	1h 24min	scthornton/qwen-coder-7b-securecode
CodeGemma 7B	7B	Google CodeGemma	1h 27min	scthornton/codegemma-7b-securecode
DeepSeek Coder 6.7B	6.7B	DeepSeek Coder	1h 15min	scthornton/deepseek-coder-6.7b-securecode
CodeLlama 13B	13B	Meta CodeLlama	1h 32min	scthornton/codellama-13b-securecode
Qwen Coder 14B	14B	Qwen 2.5 Coder	1h 19min	scthornton/qwen2.5-coder-14b-securecode
StarCoder2 15B	15B	BigCode StarCoder2	1h 40min	scthornton/starcoder2-15b-securecode
Granite 20B	20B	IBM Granite Code	1h 19min	This model

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Citation

@misc{thornton2025securecode,
  title={SecureCode v2.0: A Production-Grade Dataset for Training Security-Aware Code Generation Models},
  author={Thornton, Scott},
  year={2025},
  publisher={perfecXion.ai},
  url={https://perfecxion.ai/articles/securecode-v2-dataset-paper.html},
  note={Model: https://huggingface.co/scthornton/granite-20b-code-securecode}
}

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Links

• Dataset: scthornton/securecode (2,185 examples)
• Paper: SecureCode v2.0
• Model Collection: SecureCode Models (8 models)
• Blog Post: Training Security-Aware Code Models
• Publisher: perfecXion.ai

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License

Apache 2.0