This repo is superseded by anicka/cve-backport-codegen-qwen25-32b which has the latest v3 model. This repo is kept for legacy access to v1/v2 GGUFs and the merged safetensors.

CVE Backport Code Generation — Qwen2.5-Coder-32B (legacy)

Fine-tuned Qwen2.5-Coder-32B-Instruct for security patch backporting via per-hunk code generation.

Instead of generating unified diffs directly, this model takes a vulnerable code region and a fix description, and outputs the fixed version of the code. A programmatic diff then produces the final patch. This plays to LLM strengths in code completion and avoids format-sensitivity issues.

Quick Start

The easiest way to use this model is with the cve-backport-tool CLI, which handles the full pipeline: parse upstream patch, extract per-hunk regions, call the model, and reconstruct a unified diff.

bash

# Download and serve the model
./setup.sh

# Generate a backport patch
python3 cve-backport.py \
    --cve CVE-2024-2398 \
    --package curl-7.66.0 \
    --patch upstream.patch \
    --source-dir /path/to/curl-7.66.0/ \
    --backend openai --port 8403

GGUF Downloads

File	Quant	Size	Notes
`cve-backport-codegen-v3-q8_0.gguf`	Q8_0	33 GB	Recommended (v3, 35K dataset, 98% precision)
`cve-backport-codegen-v2-q8_0.gguf`	Q8_0	33 GB	v2 (24K dataset)
`cve-backport-codegen-v1-q8_0.gguf`	Q8_0	33 GB	v1 (17K dataset)

Evaluation

Per-hunk evaluation on held-out test cases the model never saw during training:

Metric	v1	v2	v3
Average recall	91%	94%	94%
Average precision	—	93%	98%
Exact match	—	15/20	16/20
Perfect hunks (>=95%)	16/18	17/20	17/20
Fail (<10%)	1/18	0/20	0/20

v3 was trained on 35,667 cleaned examples with LR=1e-4 (lowered from 2e-4 for stability with larger dataset). Same recall as v2, but significantly higher precision (98% vs 93%) — the model makes fewer spurious changes.

By tier:

Identical (upstream patch applies directly): 95% recall, 98% precision
Adapted (line numbers/context differ): 89% recall, 97% precision

Prompt Format

ChatML format. Each prompt covers one hunk region with 15 lines of context padding:

System:

You are a security patch backporting assistant.

Given vulnerable source code and a description of the upstream fix, output the FIXED version of the code.

Rules:
- Output ONLY the fixed code, nothing else — no explanations, no markdown fences
- Preserve exact formatting, indentation, and style of the original
- Make ONLY the changes described in the fix — do not modify anything else
- Do not add comments about what you changed

User:

## File: lib/ftp.c
## Lines: 2836-2912

```c
{vulnerable code region with 15-line padding}
```

## Fix
CVE-2017-8817: FTP wildcard matching — zero terminate the entry path

```diff
{upstream patch}
```

Assistant: The fixed code (same region with the security fix applied).

Training


Base model	Qwen2.5-Coder-32B-Instruct
Method	QLoRA (4-bit NF4, r=64, alpha=128)
Epochs	2
Learning rate	2e-4
Max sequence length	4,096 tokens
Batch size	1 (gradient accumulation 8)
Training examples	14,458 train + 2,549 eval
Training time	13.2 hours
Hardware	NVIDIA H100 NVL 94GB
Final train loss	0.0137
Final eval loss	0.00699

Files

File	Size	Description
`model-*.safetensors`	62 GB total	Full merged model (BF16)
`cve-backport-codegen-v1-q8_0.gguf`	33 GB	Q8_0 quantized GGUF for llama.cpp/ollama

The Q8_0 GGUF fits on a single L40 48GB or A100 80GB GPU with 8k context.

Usage

With the CLI tool (recommended)

The cve-backport-tool automates the full pipeline:

bash

git clone https://github.com/anicka-net/cve-backport-tool.git
cd cve-backport-tool
./setup.sh                  # downloads GGUF, registers with ollama
python3 cve-backport.py \
    --cve CVE-2024-1234 \
    --package openssl-1.1.1d \
    --patch upstream.patch \
    --source-dir /path/to/openssl-1.1.1d/

With llama-server

bash

llama-server --model cve-backport-codegen-v1-q8_0.gguf \
    --port 8403 --n-gpu-layers 99 --ctx-size 8192

With transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "anicka/cve-backport-codegen-qwen25-32b-v1",
    torch_dtype=torch.bfloat16, device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(
    "anicka/cve-backport-codegen-qwen25-32b-v1"
)

messages = [
    {"role": "system", "content": "You are a security patch backporting assistant..."},
    {"role": "user", "content": "## File: lib/url.c\n## Lines: 100-130\n\n```c\n...\n```\n\n## Fix\nCVE-2024-1234: ..."},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.0, do_sample=False)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Training Data

anicka/cve-backport-codegen-dataset — 35,667 per-hunk examples from openSUSE maintenance patches, covering 90+ packages and 2,300+ CVEs.

Intended Use

This model assists with security patch backporting in Linux distribution maintenance. It is a research tool — all generated patches must be reviewed by a maintainer before application.

License

Apache-2.0 (inherited from Qwen2.5-Coder-32B-Instruct).