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Model: Euraika/VulnLLM-R-7B-GGUF Source: Original Platform
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---
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license: apache-2.0
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base_model: Virtue-AI-HUB/VulnLLM-R-7B
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base_model_relation: quantized
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library_name: gguf
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pipeline_tag: text-generation
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tags:
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- gguf
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- llama.cpp
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- ollama
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- quantized
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- security
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- vulnerability-detection
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- reasoning
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- qwen2
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language:
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- en
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---
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# VulnLLM-R-7B — GGUF
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GGUF conversions of [**Virtue-AI-HUB/VulnLLM-R-7B**](https://huggingface.co/Virtue-AI-HUB/VulnLLM-R-7B),
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a **reasoning** model for software **vulnerability detection**, fine-tuned from
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[Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
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Paper: [arXiv:2512.07533](https://arxiv.org/abs/2512.07533).
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These files run the model locally with [`llama.cpp`](https://github.com/ggerganov/llama.cpp),
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[Ollama](https://ollama.com), [LM Studio](https://lmstudio.ai), and any GGUF runtime.
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## Files
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| File | Quant | Size | Notes |
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|------|-------|------|-------|
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| `VulnLLM-R-7B-f16.gguf` | F16 | ~15 GB | Full-precision GGUF; best quality, largest. |
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| `VulnLLM-R-7B-Q8_0.gguf` | Q8_0 | ~8 GB | 8-bit; near-lossless, recommended for most use. |
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## Model specs
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| Property | Value |
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|----------|-------|
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| Architecture | `qwen2` (Qwen2.5-7B-Instruct base) |
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| Parameters | ~7.6 B |
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| **Max context length** | **32,768 tokens (32K)** — native |
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| Layers | 28 |
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| Embedding dim | 3584 |
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| Attention | GQA, 28 Q heads / 4 KV heads |
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| Chat template | ChatML (embedded in GGUF metadata) |
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**Context notes:** 32K is the native maximum (`max_position_embeddings` / `qwen2.context_length`).
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This fine-tune ships **no `rope_scaling`**, so there is no supported YaRN path beyond 32K — pushing
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past it degrades quality. Set your runtime window (`num_ctx` / `-c`) as high as 32768; higher windows
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cost more KV-cache memory (roughly: 8K ≈ 4 GB, 16K ≈ 7 GB, 32K ≈ 15 GB on top of the ~8 GB Q8_0 weights).
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## ⚠️ This is a reasoning model — use the system prompt
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VulnLLM-R generates a step-by-step **chain-of-thought** (inside a `</think>`-delimited block)
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before its final answer. If you prompt it with a bare question and **no framing**, it can drift
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out-of-distribution and produce noisy output. Always give it the analysis framing below — ideally
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as the **system** message so it triggers reliably.
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**Recommended system prompt:**
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```
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You are an advanced vulnerability detection model. Analyze the following code
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step-by-step to determine whether it contains a security vulnerability. Reason
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about data flow, control flow, and security context, then give a final answer
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that names the vulnerability, its CWE, and how to fix it. If the code is safe, say so.
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```
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Then send the code snippet as the user message.
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**Suggested sampling:** `temperature 0.3`, `top_p 0.8` (deterministic-leaning analysis).
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## Usage
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### Ollama
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```bash
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# Pull directly from this repo (Q8_0):
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ollama pull hf.co/Euraika/VulnLLM-R-7B-GGUF:Q8_0
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# Recommended: bake in the system prompt, sampling, and full 32K context via a Modelfile:
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cat > Modelfile <<'MF'
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FROM hf.co/Euraika/VulnLLM-R-7B-GGUF:Q8_0
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SYSTEM """You are an advanced vulnerability detection model. Analyze the following code step-by-step to determine whether it contains a security vulnerability. Reason about data flow, control flow, and security context, then give a final answer that names the vulnerability, its CWE, and how to fix it. If the code is safe, say so."""
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PARAMETER temperature 0.3
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PARAMETER top_p 0.8
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PARAMETER num_ctx 32768
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MF
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ollama create vulnllm-r-7b -f Modelfile
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ollama run vulnllm-r-7b "<paste code here>"
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```
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### llama.cpp
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```bash
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llama-cli -hf Euraika/VulnLLM-R-7B-GGUF:Q8_0 --temp 0.3 --top-p 0.8 -c 32768 \
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-sys "You are an advanced vulnerability detection model. Analyze the code step-by-step, reason about data/control flow and security context, then give a final answer with the vulnerability, its CWE, and a fix." \
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-p "<paste code here>"
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```
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### Chat format (ChatML)
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```
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<|im_start|>system
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{system}<|im_end|>
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<|im_start|>user
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{code}<|im_end|>
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<|im_start|>assistant
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```
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## Conversion
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Converted from the original `safetensors` weights to GGUF and quantized to Q8_0.
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The F16 GGUF is the intermediate full-precision conversion; Q8_0 is quantized from it.
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## License & attribution
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Released under **Apache-2.0**, inherited from the base model
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[Virtue-AI-HUB/VulnLLM-R-7B](https://huggingface.co/Virtue-AI-HUB/VulnLLM-R-7B)
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(derived from Qwen2.5-7B-Instruct, Apache-2.0). All credit for the model belongs to
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the original authors; this repository only provides GGUF conversions. Please cite the
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[paper](https://arxiv.org/abs/2512.07533) and follow the upstream terms.
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```bibtex
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@article{nie2025vulnllmr,
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title={VulnLLM-R: Specialized Reasoning LLM with Agent Scaffold for Vulnerability Detection},
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author={Nie, Yuzhou and Li, Hongwei and Guo, Chengquan and Jiang, Ruizhe and Wang, Zhun and Li, Bo and Song, Dawn and Guo, Wenbo},
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journal={arXiv preprint arXiv:2512.07533},
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year={2025}
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}
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```
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3
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version https://git-lfs.github.com/spec/v1
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size 8099131840
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3
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version https://git-lfs.github.com/spec/v1
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size 15238459840
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