README.md

---
license: apache-2.0
library_name: transformers
tags:
- 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:](https://huggingface.co/models?other=base_model:quantized:ZeroXClem/Qwen2.5-7B-Qandora-CySec)

## 🚀 Model Components

- **[bunnycore/QandoraExp-7B](https://huggingface.co/bunnycore/QandoraExp-7B)**: Powerful Q&A capabilities
- **[trollek/Qwen2.5-7B-CySecButler-v0.1](https://huggingface.co/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

1. General Q&A Tasks
2. Cybersecurity Analysis
3. Hybrid Scenarios (general knowledge + cybersecurity)

## Ollama Model Card

The [GGUF quantized versions](https://huggingface.co/models?other=base_model:quantized:ZeroXClem/Qwen2.5-7B-Qandora-CySec) 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:

``` bash
ollama create qwen2.5-7B-qandora-cysec -f Modelfile
```
Once completed, you can run your ollama model by:

``` bash
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](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ZeroXClem__Qwen2.5-7B-Qandora-CySec)

|      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|