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Model: Euraika/VulnLLM-R-7B-GGUF
Source: Original Platform

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README.md

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

VulnLLM-R-7B-Q8_0.gguf

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VulnLLM-R-7B-f16.gguf

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