reaperdoesntknow

Qwen3.5-2B-CyberSec

README

License: apache-2.0

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.

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Training data may contain errors, outdated practices, or sensitive dual-use material.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.

Part of the CIx cybersecurity model collection.

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Model Details

Model Provider

reaperdoesntknow

Model Tree

Base

unsloth/Qwen3.5-2B

Fine-tuned

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality