---

license: apache-2.0

tags:

• gguf
• qwen3.5
• openmythos
• build-small-hackathon

datasets:

• build-small-hackathon/CVE_Vulnerailities_Detailed
• himanshu17HF/ArvixImport-Filtered-Final

base_model:

• build-small-hackathon/OpenMythos
• Qwen/Qwen3.6-27B

---

OpenMythos 27B - GGUF

GGUF quantisation of build-small-hackathon/OpenMythos,

a fine-tune of Qwen3.6-27B.

Converted with convert_hf_to_gguf.py --no-mtp from llama.cpp build 9658.

The fine-tune does not include MTP head weights (dropped during training), so MTP

is not available in this GGUF.

Available Quantisations

| File | Size | Type |

|------|------|------|

| OpenMythos-27B-F16.gguf | 53.8 GB | F16 |

| OpenMythos-27B-Q5_K.gguf | 18.3 GB | Q5_K_M |

| OpenMythos-27B-Q4_K.gguf | 15.4 GB | Q4_K_M |

| OpenMythos-27B-Q6_K.gguf | 21.2 GB | Q6_K |

Benchmark

Evaluated with SecEval (commit 7aef317) on 2189

multiple-choice security questions. Backend: llama.cpp OpenAI-compatible server, fully

offloaded to GPU. No chain-of-thought / reasoning enabled (enable_thinking=false).

Prompt formatted with a system prompt requesting letter-only answers (no explanation).

| Set | Model | Score |

|-----|-------|-------|

| A | OpenMythos-27B-Q5_K | 1703 / 2189 (77.8%) |

| B | VulnLLM-R-7B | 1315 / 2189 (60.1%) |

OpenMythos-27B-Q5_K test parameters

• model: OpenMythos-27B-Q5_K.gguf
• inference: temp=0.2, top_p=0.8, top_k=20, min_p=0.05, repeat_penalty=1.02
• benchmark script: /mnt/storage/SecEval-tmp/run_bench.py
• output: seceval-1781809723.json
• prompt speed: 282 tok/s | generation speed: 68 tok/s

Per-topic scores

| Topic | Score |

|-------|-------|

| PenTest | 84.2% |

| MemorySafety | 83.3% |

| WebSecurity | 82.7% |

| Vulnerability | 77.8% |

| NetworkSecurity | 77.4% |

| SoftwareSecurity | 75.0% |

| ApplicationSecurity | 74.8% |

| SystemSecurity | 73.6% |

| Cryptography | 71.4% |

VulnLLM-R-7B test parameters

• model: VulnLLM-R-7B.Q6_K.gguf
• inference: same settings as above
• output: seceval-1781811525.json
• prompt speed: 148 tok/s | generation speed: 39 tok/s

Per-topic scores

| Topic | Score |

|-------|-------|

| PenTest | 70.9% |

| WebSecurity | 66.4% |

| Vulnerability | 58.7% |

| NetworkSecurity | 58.3% |

| SystemSecurity | 56.4% |

| SoftwareSecurity | 54.7% |

| ApplicationSecurity | 54.7% |

| MemorySafety | 54.2% |

| Cryptography | 28.6% |

Full detailed results are included in this repo: seceval-1781809723.json and

seceval-1781811525.json.

Usage

llama-server (recommended)

[OpenMythos-27B]
model = /mnt/storage/models/OpenMythos/OpenMythos-27B-Q5_K.gguf
chat-template-file = /mnt/storage/llama-server/chat_template-v15.jinja
ctx-size = 65536
cache-type-k = q8_0
cache-type-v = q8_0
cache-prompt = on
cache-reuse = 2048
batch-size = 4096
ubatch-size = 4096
kv-unified = on
parallel = 1
gpu-layers = all
temp = 0.2
top-p = 0.8
top-k = 20
min-p = 0.05
presence-penalty = 0.2
repeat-penalty = 1.02
spec-type = ngram-mod
spec-draft-n-max = 5
reasoning-format = deepseek
swa-checkpoints = 5

llama-cli

/mnt/storage/llama.cpp/build/bin/llama-cli \
  -m /mnt/storage/models/OpenMythos/OpenMythos-27B-Q5_K.gguf \
  --chat-template-file /mnt/storage/llama-server/chat_template-v15.jinja \
  -c 65536 -b 4096 --ubatch-size 4096 \
  --cache-type-k q8_0 --cache-type-v q8_0 \
  --kv-unified -t 8 -fa \
  --temp 0.2 --top-p 0.8 --top-k 20 --min-p 0.05 \
  --presence-penalty 0.2 --repeat-penalty 1.02 \
  -ngl all \
  -p "Your prompt here"