---
license: cc-by-nc-sa-4.0
language:
  - en
base_model: ivitopow/secucoder
tags:
  - code
  - security
  - python
  - gguf
  - ollama
  - llama-cpp
  - cybersecurity
  - secure-coding
  - quantized
task_categories:
  - text-generation
---

# SecuCoder — GGUF

Quantized GGUF version of [SecuCoder](https://huggingface.co/ivitopow/secucoder), a fine-tuned Llama 3.1 8B Instruct model for secure Python code generation and vulnerability remediation.

For full model details, training methodology, and evaluation results, see the [main model card](https://huggingface.co/ivitopow/secucoder).

---

## Available Files

| File | Quantization | Size | Use case |
|---|---|---|---|
| `secucoder-Q4_K_M.gguf` | Q4_K_M | ~4.6 GB | Recommended — best balance of quality and size |

---

## Usage with Ollama

**1. Download the Modelfile from this repo and create the model:**

```bash
ollama create secucoder -f Modelfile
```

**2. Run it:**

```bash
ollama run secucoder
```

**3. Or via API:**

```bash
curl http://localhost:11434/api/generate -d '{
  "model": "secucoder",
  "prompt": "Fix the security vulnerability in this Python code.\n\n```python\nname = request.args.get(\"name\")\nresp = make_response(\"Your name is \" + name)\n```\n\nCWE: CWE-079",
  "stream": false
}'
```

---

## Usage with llama.cpp

```bash
./llama-cli \
  -m secucoder-Q4_K_M.gguf \
  --ctx-size 4096 \
  --temp 0.1 \
  --top-p 0.9 \
  -p "You are a secure Python assistant. Fix the vulnerability in this code: ..."
```

---

## Recommended Parameters

| Parameter | Value |
|---|---|
| `temperature` | 0.1 |
| `top_p` | 0.9 |
| `num_ctx` | 4096 |
| `num_predict` | 3072 |

---

## System Prompt

```
You are a secure Python assistant. Help identify, explain, and fix security issues in Python code. Prefer safe, practical, and production-ready solutions.
```

---

## Evaluation

The full SecuCoder system (Q4 + structured prompting + RAG) achieves an overall score of **77.11** vs **60.34** for the untuned Llama 3.1 8B baseline — a **+27.8% improvement** measured by weighted static analysis findings (Bandit + Semgrep).

| Variant | Overall Score |
|---|---|
| Llama 3.1 8B Instruct (baseline) | 60.34 |
| SecuCoder Q4 (this model) | 61.46 |
| SecuCoder Q4 + structured prompt | 64.46 |
| SecuCoder Q4 + structured prompt + RAG | **77.11** |

---

## Related

| Resource | Link |
|---|---|
| Full model (safetensors) | [ivitopow/secucoder](https://huggingface.co/ivitopow/secucoder) |
| Training dataset | [ivitopow/secucoder](https://huggingface.co/datasets/ivitopow/secucoder) |
| Base model | [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) |

---

## License

Released under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). Built on Llama 3.1, subject to [Meta's Llama 3 Community License](https://llama.meta.com/llama3/license/).