merge
mergekit
lazymergekit
bunnycore/QandoraExp-7B
trollek/Qwen2.5-7B-CySecButler-v0.1 base_model:
bunnycore/QandoraExp-7B
trollek/Qwen2.5-7B-CySecButler-v0.1 model-index:
name: Qwen2.5-7B-Qandora-CySec results:
- task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics:
  - type: inst_level_strict_acc and prompt_level_strict_acc value: 67.73 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-Qandora-CySec name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics:
  - type: acc_norm value: 36.26 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-Qandora-CySec name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics:
  - type: exact_match value: 22.89 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-Qandora-CySec name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics:
  - type: acc_norm value: 6.71 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-Qandora-CySec name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics:
  - type: acc_norm value: 13.41 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-Qandora-CySec name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics:
  - type: acc value: 38.72 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-Qandora-CySec name: Open LLM Leaderboard

Qwen2.5-7B-Qandora-CySec

ZeroXClem/Qwen2.5-7B-Qandora-CySec is an advanced model merge combining Q&A capabilities and cybersecurity expertise using the mergekit framework. This model excels in both general question-answering tasks and specialized cybersecurity domains.

🔬 Quants

ZeroXClem/Qwen2.5-7B-Qandora-CySec quantized in GGUF format can be found here:

🚀 Model Components

bunnycore/QandoraExp-7B: Powerful Q&A capabilities
trollek/Qwen2.5-7B-CySecButler-v0.1: Specialized cybersecurity knowledge

🧩 Merge Configuration

The models are merged using spherical linear interpolation (SLERP) for optimal blending:

yaml

slices:
  - sources:
      - model: bunnycore/QandoraExp-7B
        layer_range: [0, 28]
      - model: trollek/Qwen2.5-7B-CySecButler-v0.1
        layer_range: [0, 28]
merge_method: slerp
base_model: bunnycore/QandoraExp-7B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

Key Parameters

Self-Attention (self_attn): Controls blending across self-attention layers
MLP: Adjusts Multi-Layer Perceptron balance
Global Weight (t.value): 0.5 for equal contribution from both models
Data Type: bfloat16 for efficiency and precision

🎯 Applications

General Q&A Tasks
Cybersecurity Analysis
Hybrid Scenarios (general knowledge + cybersecurity)

Ollama Model Card

The GGUF quantized versions can be used directly in Ollama using the following model card. Simple save as Modelfile in the same directory.

Modelfile

FROM ./qwen2.5-7b-qandora-cysec-q5_0.gguf  # Change to your specific quant

# set the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 0.7
PARAMETER top_p 0.8
PARAMETER repeat_penalty 1.05
PARAMETER top_k 20

TEMPLATE """{{ if .Messages }}
{{- if or .System .Tools }}<|im_start|>system
{{ .System }}
{{- if .Tools }}

# Tools

You are provided with function signatures within <tools></tools> XML tags:
<tools>{{- range .Tools }}
{"type": "function", "function": {{ .Function }}}{{- end }}
</tools>

For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call>
{{- end }}<|im_end|>
{{ end }}
{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 -}}
{{- if eq .Role "user" }}<|im_start|>user
{{ .Content }}<|im_end|>
{{ else if eq .Role "assistant" }}<|im_start|>assistant
{{ if .Content }}{{ .Content }}
{{- else if .ToolCalls }}<tool_call>
{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
{{ end }}</tool_call>
{{- end }}{{ if not $last }}<|im_end|>
{{ end }}
{{- else if eq .Role "tool" }}<|im_start|>user
<tool_response>
{{ .Content }}
</tool_response><|im_end|>
{{ end }}
{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
{{ end }}
{{- end }}
{{- else }}
{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""

# set the system message
SYSTEM """You are Qwen, merged by ZeroXClem. As such, you are a high quality assistant that excels in general question-answering tasks, code generation, and specialized cybersecurity domains."""

Then create the ollama model by running:

ollama create qwen2.5-7B-qandora-cysec -f Modelfile

Once completed, you can run your ollama model by:

ollama run qwen2.5-7B-qandora-cysec

🛠 Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "ZeroXClem/Qwen2.5-7B-Qandora-CySec"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What are the fundamentals of python programming?"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output = model.generate(input_ids, max_length=100)
response = tokenizer.decode(output[0], skip_special_tokens=True)
print(response)

📜 License

This model inherits the licenses of its base models. Refer to bunnycore/QandoraExp-7B and trollek/Qwen2.5-7B-CySecButler-v0.1 for usage terms.

🙏 Acknowledgements

bunnycore (QandoraExp-7B)
trollek (Qwen2.5-7B-CySecButler-v0.1)
mergekit project

📚 Citation

If you use this model, please cite this repository and the original base models.

💡 Tags

merge, mergekit, lazymergekit, bunnycore/QandoraExp-7B, trollek/Qwen2.5-7B-CySecButler-v0.1, cybersecurity, Q&A

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	30.95
IFEval (0-Shot)	67.73
BBH (3-Shot)	36.26
MATH Lvl 5 (4-Shot)	22.89
GPQA (0-shot)	6.71
MuSR (0-shot)	13.41
MMLU-PRO (5-shot)	38.72

RichardErkhov/ZeroXClem_-_Qwen2.5-7B-Qandora-CySec-8bits

Original model description: