Instructions to use xtools-at/Qwen3.5-4B-Qwensploit-CyberSecurity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xtools-at/Qwen3.5-4B-Qwensploit-CyberSecurity with Transformers:

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

pipe = pipeline("image-text-to-text", model="xtools-at/Qwen3.5-4B-Qwensploit-CyberSecurity")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("xtools-at/Qwen3.5-4B-Qwensploit-CyberSecurity")
model = AutoModelForMultimodalLM.from_pretrained("xtools-at/Qwen3.5-4B-Qwensploit-CyberSecurity", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use xtools-at/Qwen3.5-4B-Qwensploit-CyberSecurity with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xtools-at/Qwen3.5-4B-Qwensploit-CyberSecurity with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xtools-at/Qwen3.5-4B-Qwensploit-CyberSecurity"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xtools-at/Qwen3.5-4B-Qwensploit-CyberSecurity",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/xtools-at/Qwen3.5-4B-Qwensploit-CyberSecurity

SGLang

How to use xtools-at/Qwen3.5-4B-Qwensploit-CyberSecurity 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 "xtools-at/Qwen3.5-4B-Qwensploit-CyberSecurity" \
    --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": "xtools-at/Qwen3.5-4B-Qwensploit-CyberSecurity",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "xtools-at/Qwen3.5-4B-Qwensploit-CyberSecurity" \
        --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": "xtools-at/Qwen3.5-4B-Qwensploit-CyberSecurity",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use xtools-at/Qwen3.5-4B-Qwensploit-CyberSecurity with Docker Model Runner:
```
docker model run hf.co/xtools-at/Qwen3.5-4B-Qwensploit-CyberSecurity
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwensploit 3.5 4B: Cybersecurity and Red Team

Qwensploit is a cybersecurity-focused fine-tune of a Qwen3.5-4B model merge, designed for security reasoning, vulnerability analysis, secure coding, red-team knowledge, and technical question answering.

The model was fine-tuned using LoRA on 7,000 training examples drawn from four datasets, with an additional 350 evaluation examples.

Base model: xtools-at/Qwen3.5-4B-Claude-3xMerge-Jackrong-UNCENSORED-heretic
Method: Supervised fine-tuning (LoRA)
License metadata: Apache-2.0, subject to upstream license compatibility review
All Qwensploit models:

Intended use

Designed to assist with:

Cybersecurity research and technical question answering.
Vulnerability analysis and secure code review.
Defensive security, threat modeling, and incident analysis.
Authorized penetration testing and red-team exercises.
CTF challenges, security education, and programming assistance.

Outputs may be incorrect or unsafe. Validate generated code and recommendations, and use offensive-security capabilities only in authorized environments.

Training datasets

The training mixture was randomly selected and shuffled, with a maximum entry length of 8,000.

Dataset	Entries	Share
CyberSec-Reasoning-Premium	2,305	33%
RedTeam-Premium	2,385	34%
Claude Mythos Distilled 25K — Clean	1,385	20%
Qwen3.8-Max Distillation 50K — Clean	925	13%
Total	7,000	100%

The mixture combines cybersecurity and red-team examples with synthetic reasoning, coding, and general instruction-following data.

Upstream dataset sources

The datasets are primarily built from the following second-level sources:

CyberSec-Reasoning-Premium: Cybersecurity Reasoning Dataset, among other sources.
RedTeam-Premium: Ultimate Offensive Red Team.
Claude Mythos Distilled: WithinUsAI/claude_mythos_distilled_25k.
Qwen distillation: r0b0tlab/qwen3.8-max-distillation-50k.

Consult all linked cards for full source provenance, dataset construction details, and licensing terms.

Base model lineage

The direct base model is Qwen3.5-4B-Claude-3xMerge-Jackrong-UNCENSORED-heretic.

The related Claude 3xMerge Jackrong model is a merge combining reasoning distillation, coding, and multi-step reasoning fine-tunes distilled from Claude Opus. The underlying model family is Qwen3.5-4B.

Training configuration

Parameter	Value
Epochs	1
Training entries	7,000
Evaluation entries	350
Context length	8,192
Learning rate	2e-4
Batch size	4x8
LoRA rank (`r`)	16
LoRA alpha	32
LoRA dropout	0.02
LoRA traget modules	all
Optimizer	AdamW 8-bit
LR scheduler	Linear
Weight decay	0.002
GPU	NVIDIA A100 40 GB

Training results

Reported final metrics:

Metric	Value
Training loss	0.524
Evaluation loss	0.532

Loss tracking by training step

Metric \ Step	Step 48	Step 96	Step 144	Step 192	Step 219
Training loss	0.695	0.699	0.546	0.530	0.524
Evaluation loss	0.700	0.604	0.558	0.537	0.532

Loss is a training diagnostic, not a direct measure of cybersecurity accuracy or safety. Independent benchmarks are needed to assess performance.

Limitations and safety

May generate inaccurate explanations, insecure code, or outdated security advice.
Synthetic distillation data may contain teacher-model errors.
Performance on real-world security tasks has not been independently established.
Use only on systems for which testing is explicitly authorized.
Review generated commands, code, and recommendations before use.

Licensing

Although this model's is licensed under Apache-2.0, upstream licensing must be reviewed before redistribution or commercial use. In particular, the Qwen distillation dataset documents inherited restrictions that may affect downstream use. Review the base-model and dataset cards and for license information if necessary.