Instructions to use reaperdoesntknow/Qwen3.5-2B-CyberSec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use reaperdoesntknow/Qwen3.5-2B-CyberSec with Transformers:

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

pipe = pipeline("text-generation", model="reaperdoesntknow/Qwen3.5-2B-CyberSec")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("reaperdoesntknow/Qwen3.5-2B-CyberSec")
model = AutoModelForMultimodalLM.from_pretrained("reaperdoesntknow/Qwen3.5-2B-CyberSec", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use reaperdoesntknow/Qwen3.5-2B-CyberSec with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "reaperdoesntknow/Qwen3.5-2B-CyberSec"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "reaperdoesntknow/Qwen3.5-2B-CyberSec",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/reaperdoesntknow/Qwen3.5-2B-CyberSec

SGLang

How to use reaperdoesntknow/Qwen3.5-2B-CyberSec 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 "reaperdoesntknow/Qwen3.5-2B-CyberSec" \
    --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": "reaperdoesntknow/Qwen3.5-2B-CyberSec",
		"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 "reaperdoesntknow/Qwen3.5-2B-CyberSec" \
        --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": "reaperdoesntknow/Qwen3.5-2B-CyberSec",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use reaperdoesntknow/Qwen3.5-2B-CyberSec with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for reaperdoesntknow/Qwen3.5-2B-CyberSec to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for reaperdoesntknow/Qwen3.5-2B-CyberSec to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for reaperdoesntknow/Qwen3.5-2B-CyberSec to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="reaperdoesntknow/Qwen3.5-2B-CyberSec",
    max_seq_length=2048,
)

Docker Model Runner
How to use reaperdoesntknow/Qwen3.5-2B-CyberSec with Docker Model Runner:
```
docker model run hf.co/reaperdoesntknow/Qwen3.5-2B-CyberSec
```

Qwen3.5-2B-CyberSec / README.md

reaperdoesntknow

keeper: refresh 2026-08-18

1577edc verified 1 day ago

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	---
	license: apache-2.0
	base_model: unsloth/Qwen3.5-2B
	language:
	- en
	datasets:
	- Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- transformers
	- safetensors
	- unsloth
	- qwen3_5
	- cybersecurity
	- conversational
	---

	# Qwen3.5-2B-CyberSec

	An English Qwen3.5 2B checkpoint associated with the Trendyol Cybersecurity Instruction Tuning Dataset and exported in Transformers / Safetensors format.

	This release is intended for research and local experimentation. The repository does not currently publish benchmark or safety-evaluation results, so the model should not be treated as a validated cybersecurity authority.

	## Lineage

	- Immediate base: [unsloth/Qwen3.5-2B](https://huggingface.co/unsloth/Qwen3.5-2B)
	- Upstream family: Qwen3.5 2B
	- Dataset recorded in repository metadata: [Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset](https://huggingface.co/datasets/Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset)
	- Format: Transformers / Safetensors
	- License: Apache-2.0

	## Load with Transformers

	The configuration identifies a Qwen3.5 conditional-generation architecture with text and vision components. Use a recent Transformers release that supports this architecture.

	```python
	from transformers import AutoModelForMultimodalLM, AutoProcessor

	model_id = "reaperdoesntknow/Qwen3.5-2B-CyberSec"

	processor = AutoProcessor.from_pretrained(model_id)
	model = AutoModelForMultimodalLM.from_pretrained(
	model_id,
	device_map="auto",
	)

	messages = [
	{"role": "user", "content": [{"type": "text", "text": "Explain least privilege."}]}
	]
	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)
	answer = outputs[0][inputs["input_ids"].shape[-1]:]
	print(processor.decode(answer, skip_special_tokens=True))
	```

	Dependency and device behavior can vary across Transformers versions. Pin a tested environment for reproducible use.

	## Intended use

	- Research on small-model responses to cybersecurity instruction prompts.
	- Local prototyping and qualitative evaluation.
	- Comparison with the upstream Qwen3.5 2B checkpoint.
	- Conversion and quantization experiments.

	## Evaluation status

	No versioned benchmark report, baseline comparison, held-out test result, or safety evaluation is published in the repository reviewed for this card. Dataset association and a successful model export do not establish improved cybersecurity performance.

	Useful next evidence would include:

	- Results against the unchanged upstream base model.
	- Held-out cybersecurity QA and scenario tests.
	- General-capability regression checks.
	- Hallucination, harmful-output, and misuse evaluations.
	- A reproducible evaluation harness and exact revision hashes.

	## Limitations and safety

	- The model can generate incorrect or unsafe technical guidance.
	- Training data may contain errors, outdated practices, or sensitive dual-use material.
	- The public card does not document dataset preprocessing, contamination checks, training hyperparameters, or checkpoint-selection criteria.
	- Do not execute generated commands without review and isolation.
	- Do not use the model as the sole basis for incident response, vulnerability disclosure, access-control, or other consequential security decisions.

	## GGUF build

	For local GGUF variants, see [reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF).

	Part of the [CIx cybersecurity model collection](https://huggingface.co/collections/reaperdoesntknow/cix-cybersecurity-models).
	<!-- cix-keeper-ts:2026-08-18T13:16:42Z -->