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.