Instructions to use ZeroXClem/Qwen2.5-7B-Qandora-CySec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZeroXClem/Qwen2.5-7B-Qandora-CySec with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ZeroXClem/Qwen2.5-7B-Qandora-CySec")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ZeroXClem/Qwen2.5-7B-Qandora-CySec")
model = AutoModelForCausalLM.from_pretrained("ZeroXClem/Qwen2.5-7B-Qandora-CySec")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ZeroXClem/Qwen2.5-7B-Qandora-CySec with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZeroXClem/Qwen2.5-7B-Qandora-CySec"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZeroXClem/Qwen2.5-7B-Qandora-CySec",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ZeroXClem/Qwen2.5-7B-Qandora-CySec

SGLang

How to use ZeroXClem/Qwen2.5-7B-Qandora-CySec with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ZeroXClem/Qwen2.5-7B-Qandora-CySec" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZeroXClem/Qwen2.5-7B-Qandora-CySec",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ZeroXClem/Qwen2.5-7B-Qandora-CySec" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZeroXClem/Qwen2.5-7B-Qandora-CySec",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ZeroXClem/Qwen2.5-7B-Qandora-CySec with Docker Model Runner:
```
docker model run hf.co/ZeroXClem/Qwen2.5-7B-Qandora-CySec
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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:

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.

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

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)