---

license: apache-2.0

language:

• zh
• en

tags:

• safetensors
• qwen2
• red-team
• cybersecurity
• code-audit
• vulnerability-discovery
• exploit-development
• think-chain
• grpo
• pytorch
• text-generation-inference
• region:us
• llama-cpp
• gguf-my-repo

pipeline_tag: text-generation

base_model: Tidecaller/BountyHunter-RedTeam

widget:

• text: "<|im_start|>system\nYou are BountyHunter, an elite red-team security model\

\ developed by Security Researcher Tidecaller. Use <think> reasoning chains for\

\ security analysis.<|im_end|>\n<|im_start|>user\nAudit this C code for vulnerabilities:\n\

```c\nvoid copy_data(char dest, const char src) {\n strcpy(dest, src);\n\

}\nint main(int argc, char** argv) {\n char buf[64];\n copy_data(buf, argv[1]);\n\

\ return 0;\n}\n```<|im_end|>\n<|im_start|>assistant"

example_title: 🔍 Code Audit

• text: '<|im_start|>system

You are BountyHunter, an elite red-team security model developed by Security Researcher

Tidecaller. Use <think> reasoning chains for security analysis.<|im_end|>

<|im_start|>user

Explain the steps to perform a SQL injection attack and how to defend against

it.<|im_end|>

<|im_start|>assistant'

example_title: ⚔️ Security Knowledge

model-index:

• name: BountyHunter-RedTeam

results:

- task:

type: text-generation

name: MMLU (57 subjects)

dataset:

name: mmlu

type: cais/mmlu

metrics:

- type: acc

value: 68.8

name: MMLU Average Accuracy

- task:

type: text-generation

name: HellaSwag

dataset:

name: hellaswag

type: Rowan/hellaswag

metrics:

- type: acc_norm

value: 76.42

name: HellaSwag Accuracy

- task:

type: text-generation

name: ARC-Challenge

dataset:

name: arc_challenge

type: allenai/ai2_arc

metrics:

- type: acc_norm

value: 58.36

name: ARC-C Accuracy

- task:

type: text-generation

name: Winogrande

dataset:

name: winogrande

type: winogrande

metrics:

- type: acc

value: 73.56

name: Winogrande Accuracy

- task:

type: text-generation

name: PIQA

dataset:

name: piqa

type: piqa

metrics:

- type: acc_norm

value: 78.78

name: PIQA Accuracy

- task:

type: text-generation

name: BoolQ

dataset:

name: boolq

type: boolq

metrics:

- type: acc

value: 88.07

name: BoolQ Accuracy

- task:

type: text-generation

name: TruthfulQA MC2

dataset:

name: truthfulqa_mc2

type: truthfulqa

metrics:

- type: acc

value: 54.6

name: TruthfulQA MC2 Accuracy

- task:

type: text-generation

name: HumanEval

dataset:

name: humaneval

type: openai_humaneval

metrics:

- type: pass@1

value: 42.68

name: HumanEval pass@1

- task:

type: text-generation

name: WMDP (Weapons of Mass Destruction Proxy)

dataset:

name: wmdp

type: cais/wmdp

metrics:

- type: acc

value: 59.13

name: WMDP Overall

- type: acc

value: 72.19

name: WMDP Biology

- type: acc

value: 50.0

name: WMDP Chemistry

- type: acc

value: 52.64

name: WMDP Cybersecurity

- task:

type: text-generation

name: HarmBench (Safety Compliance)

dataset:

name: harmbench

type: centerforaisafety/harmbench

metrics:

- type: asr

value: 10.94

name: HarmBench Overall ASR (↓)

- type: asr

value: 1.89

name: HarmBench Standard ASR (↓)

- type: asr

value: 26.25

name: HarmBench Copyright ASR (↓)

- type: asr

value: 13.58

name: HarmBench Contextual ASR (↓)

- task:

type: text-generation

name: PrimeVul (Vulnerability Detection)

dataset:

name: primevul

type: ASSERT-KTH/PrimeVul

metrics:

- type: f1

value: 65.0

name: PrimeVul Detection F1

- type: recall

value: 88.5

name: PrimeVul Detection Recall

- type: precision

value: 51.4

name: PrimeVul Detection Precision

- type: acc

value: 5.7

name: PrimeVul CWE Classification

- type: acc

value: 28.5

name: PrimeVul Paired Comparison

---

ckuethe/BountyHunter-RedTeam-Q6_K-GGUF

This model was converted to GGUF format from Tidecaller/BountyHunter-RedTeam using llama.cpp via the ggml.ai's GGUF-my-repo space.

Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo ckuethe/BountyHunter-RedTeam-Q6_K-GGUF --hf-file bountyhunter-redteam-q6_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo ckuethe/BountyHunter-RedTeam-Q6_K-GGUF --hf-file bountyhunter-redteam-q6_k.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo ckuethe/BountyHunter-RedTeam-Q6_K-GGUF --hf-file bountyhunter-redteam-q6_k.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo ckuethe/BountyHunter-RedTeam-Q6_K-GGUF --hf-file bountyhunter-redteam-q6_k.gguf -c 2048