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 about 15 hours ago

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metadata

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
Upstream family: Qwen3.5 2B
Dataset recorded in repository metadata: 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.

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.

Part of the CIx cybersecurity model collection.