| --- |
| library_name: gguf |
| license: other |
| base_model: google/gemma-4-e4b-it |
| tags: |
| - gguf |
| - gemma4 |
| - gemma |
| - unsloth |
| - social-engineering |
| - cybersecurity |
| - phishing |
| - red-team |
| - conversational |
| - fine-tuned |
| - llama.cpp |
| pipeline_tag: text-generation |
| language: |
| - en |
| - fa |
| datasets: |
| - smd20/social-engineering-qa-english |
| - smd20/social-engineering-qa-persian |
| --- |
| |
| # Social Engineering Specialist — Gemma 4 E4B (GGUF) |
|
|
| **`smd20/socialengineering`** is a domain-specialized conversational model for **social engineering, |
| phishing awareness, and red-team education**, fine-tuned from **Google Gemma 4 E4B** |
| using [Unsloth](https://github.com/unslothai/unsloth) and exported as **BF16 GGUF** |
| for efficient local deployment with `llama.cpp`, Ollama, LM Studio, and related runtimes. |
|
|
| The model was trained on a large bilingual Q&A corpus derived from authoritative |
| social-engineering reference books, covering definitions, attack techniques |
| (phishing, vishing, pretexting, baiting, tailgating), case studies, and defensive |
| strategies. |
|
|
| --- |
|
|
| ## Model Summary |
|
|
| | Property | Value | |
| | --- | --- | |
| | **Base architecture** | Gemma 4 (E4B instruction-tuned variant) | |
| | **Parameters** | ~8B | |
| | **Precision / format** | BF16 GGUF | |
| | **Primary weight file** | `unsloth-gemma-4-E4B-it.BF16.gguf` | |
| | **Multimodal projector** | `unsloth-gemma-4-E4B-it.BF16-mmproj.gguf` | |
| | **Fine-tuning framework** | [Unsloth](https://github.com/unslothai/unsloth) | |
| | **Domain** | Social engineering, phishing, red-team awareness | |
| | **Languages** | English, Persian (Farsi) | |
| | **Context length (training)** | 2,048 tokens | |
| | **Repository** | [smd20/socialengineering](https://huggingface.co/smd20/socialengineering) | |
|
|
| --- |
|
|
| ## Intended Use |
|
|
| ### Primary use cases |
|
|
| - Organizational **security-awareness chatbots** |
| - **Phishing and social-engineering education** for analysts and end users |
| - **Red-team / blue-team training** scenarios in controlled environments |
| - Local, privacy-preserving Q&A over social-engineering concepts |
|
|
| ### Out-of-scope / misuse |
|
|
| This model is **not** a substitute for legal, operational, or incident-response |
| authority. It must **not** be used to conduct unauthorized attacks, harvest credentials, |
| or deceive individuals outside approved training and research contexts. |
|
|
| --- |
|
|
| ## Training Procedure |
|
|
| Fine-tuning was performed in **Unsloth Studio** on top of **`gemma-4-E4B`**, using a |
| bilingual social-engineering Q&A corpus built from structured knowledge articles |
| extracted from eight reference books. |
|
|
| ### Training hyperparameters |
|
|
| | Setting | Value | |
| | --- | --- | |
| | Epochs | 30 | |
| | Learning rate | `2.0e-4` | |
| | Context length | 2,048 | |
| | LoRA rank | 16 | |
| | LoRA dropout | 0.16 | |
| | LoRA target modules | All enabled (`Enable LoRA`) | |
| | Optimizer | AdamW 8-bit | |
| | LR scheduler | Linear | |
| | Weight decay | 0.001 | |
|
|
| ### Export configuration |
|
|
| | Setting | Value | |
| | --- | --- | |
| | Training run | `gemma-4-E4B` | |
| | Export method | GGUF (quantized export path) | |
| | Published precision | BF16 | |
| | Main artifact | `unsloth-gemma-4-E4B-it.BF16.gguf` | |
|
|
| The published checkpoint preserves the merged fine-tuned weights in GGUF form for |
| deployment with `llama.cpp`-compatible runtimes. |
|
|
| --- |
|
|
| ## Training Data |
|
|
| The model was trained on conversational Q&A pairs grounded in curated social-engineering |
| knowledge. The underlying datasets are publicly released on Hugging Face: |
|
|
| | Dataset | URL | Records | |
| | --- | --- | ---: | |
| | English Q&A | [https://huggingface.co/datasets/smd20/social-engineering-qa-english](https://huggingface.co/datasets/smd20/social-engineering-qa-english) | 3,330 | |
| | Persian Q&A | [https://huggingface.co/datasets/smd20/social-engineering-qa-persian](https://huggingface.co/datasets/smd20/social-engineering-qa-persian) | 3,330 | |
|
|
| ### Reference corpora |
|
|
| Knowledge articles were derived from the following legally acquired books: |
|
|
| - Deep Insight into Social Engineering |
| - ESET Social Engineering Handbook |
| - Learn Social Engineering: Learn the Art of Human Hacking (Erdal Ozkaya) |
| - Social Engineering: How Crowdmasters, Phreaks, Hackers (Gehl & Lawson) |
| - Social Engineering in Cybersecurity: Threats and Defenses (Gururaj et al.) |
| - Social Engineering: The Science of Human Hacking (Christopher Hadnagy) |
| - Social Engineering: The Art of Human Hacking (Christopher Hadnagy) |
| - Sefreta: Zero to Hundred Social Engineering (Persian) |
|
|
| ### Corpus construction pipeline |
|
|
| 1. Controlled segmentation of reference books |
| 2. Schema-driven knowledge article generation (JSONL) |
| 3. Grounded bilingual Q&A generation with strict source constraints |
| 4. Global deduplication and bilingual split |
|
|
| ### Training Corpus Overview |
|
|
| | Metric | Value | |
| | --- | ---: | |
| | English Q&A records | 3,330 | |
| | Persian Q&A records | 3,330 | |
| | Bilingual question units | 3,330 | |
| | Total bilingual records (EN + FA) | 6,660 | |
| | Structured knowledge articles | 1,165 | |
| | Article coverage | 1,163 / 1,165 (99.8%) | |
| | Reference books | 8 | |
| | Deduplicated v1 duplicates skipped | 159 | |
| |
| ### Character-Length Statistics |
| |
| | Split | Field | Mean | Median | Std. Dev. | Min | Max | |
| | --- | --- | ---: | ---: | ---: | ---: | ---: | |
| | English | Question | 96.56 | 95.0 | 21.98 | 23 | 199 | |
| | English | Answer | 180.12 | 171.0 | 80.13 | 3 | 827 | |
| | Persian | Question | 81.08 | 80.0 | 21.76 | 12 | 181 | |
| | Persian | Answer | 163.48 | 153.0 | 74.06 | 3 | 481 | |
| | Combined (EN+FA) | Question | 88.82 | 88.0 | 23.2 | 12 | 199 | |
| | Combined (EN+FA) | Answer | 171.8 | 161.0 | 77.6 | 3 | 827 | |
| |
| ### Knowledge Articles per Reference Book |
| |
| | Reference Book (internal ID) | Knowledge Articles | |
| | --- | ---: | |
| | Learn-Social-Engineering-Learn-the-Art-of-Human-Hacking-Dr.-Erdal-Ozkaya-_-WeLib.org-__FULL | 397 | |
| | Social-Engineering-Science-Hacking-Hadnagy_FULL | 239 | |
| | Social-Engineering-Cybersecurity-Gururaj_FULL | 212 | |
| | Social-Engineering-Crowdmasters-Gehl-Lawson_FULL | 206 | |
| | Sefreta-Social-Engineering_FULL | 55 | |
| | ESET-Social_engineering_handbook_FULL | 28 | |
| | Social-Engineering-Art-Hacking-Hadnagy_FULL | 21 | |
| | deep-insight-into-social-engineering_FULL | 7 | |
| |
| --- |
|
|
| ## Evaluation & Limitations |
|
|
| - The model inherits base-model limitations and may **hallucinate** on out-of-domain queries. |
| - Training data were LLM-assisted and should be complemented with human review for |
| high-stakes deployments. |
| - Copyright of source books remains with publishers; released datasets contain **derived |
| annotations only**. |
| - BF16 GGUF requires approximately **15.1 GB** VRAM/RAM for full-precision loading. |
|
|
| --- |
|
|
| ## How to Download from Hugging Face |
|
|
| ### Option 1 — `huggingface_hub` (recommended) |
| |
| ```python |
| from huggingface_hub import hf_hub_download |
|
|
| repo_id = "smd20/socialengineering" |
| token = None # set HF_TOKEN if the repo is private |
|
|
| model_path = hf_hub_download( |
| repo_id=repo_id, |
| filename="unsloth-gemma-4-E4B-it.BF16.gguf", |
| token=token, |
| ) |
| mmproj_path = hf_hub_download( |
| repo_id=repo_id, |
| filename="unsloth-gemma-4-E4B-it.BF16-mmproj.gguf", |
| token=token, |
| ) |
| |
| print("Model:", model_path) |
| print("MMProj:", mmproj_path) |
| ``` |
| |
| ### Option 2 — Snapshot download |
| |
| ```python |
| from huggingface_hub import snapshot_download |
|
|
| local_dir = snapshot_download( |
| repo_id="smd20/socialengineering", |
| allow_patterns=["*.gguf"], |
| ) |
| print("Downloaded to:", local_dir) |
| ``` |
| |
| ### Option 3 — CLI |
|
|
| ```bash |
| huggingface-cli download smd20/socialengineering \ |
| unsloth-gemma-4-E4B-it.BF16.gguf \ |
| unsloth-gemma-4-E4B-it.BF16-mmproj.gguf |
| ``` |
|
|
| --- |
|
|
| ## Inference Examples |
|
|
| ### `llama.cpp` |
|
|
| ```bash |
| llama-cli -hf smd20/socialengineering:BF16 --jinja |
| ``` |
|
|
| For multimodal usage: |
|
|
| ```bash |
| llama-mtmd-cli -hf smd20/socialengineering:BF16 --jinja |
| ``` |
|
|
| ### `llama-cpp-python` |
|
|
| ```python |
| from llama_cpp import Llama |
| |
| llm = Llama.from_pretrained( |
| repo_id="smd20/socialengineering", |
| filename="unsloth-gemma-4-E4B-it.BF16-mmproj.gguf", |
| ) |
| |
| response = llm.create_chat_completion( |
| messages=[ |
| { |
| "role": "user", |
| "content": "What is pretexting in social engineering, and how does it differ from impersonation?", |
| } |
| ], |
| ) |
| print(response["choices"][0]["message"]["content"]) |
| ``` |
|
|
| ### Ollama |
|
|
| ```bash |
| ollama run hf.co/smd20/socialengineering:BF16 |
| ``` |
|
|
| --- |
|
|
| ## Authorship, Ownership, and Legal Notice |
|
|
| **Legal owner and maintainer:** **Samad Sohrab** — PhD Student in Artificial Intelligence. |
|
|
| This model checkpoint, its associated training configuration, and the derived Q&A |
| datasets released under the `smd20` Hugging Face namespace are authored and |
| maintained by **Samad Sohrab**. All rights in the model card, training pipeline |
| documentation, and derived dataset annotations are reserved by the author unless |
| otherwise stated in the repository license. |
|
|
| Source-book copyrights remain with their respective publishers. This repository |
| distributes **fine-tuned model weights** and **derived instructional annotations** only. |
|
|
| --- |
|
|
| ## Acknowledgments |
|
|
| This work was conducted under the research supervision of **Dr. Amir Nezami Safa**, |
| who served as academic advisor throughout dataset construction, model fine-tuning, |
| and publication. His guidance on methodology, reproducibility, and scientific rigor |
| was instrumental to this release. |
|
|
| Training infrastructure used [Unsloth](https://github.com/unslothai/unsloth) for |
| efficient Gemma 4 fine-tuning and GGUF export. |
|
|
| --- |
|
|
| ## Citation |
|
|
| If you use this model or the associated datasets in academic work, please cite: |
|
|
| ```bibtex |
| @misc{sohrab2026socialengineering, |
| author = {Sohrab, Samad and Nazami Saffa, Amir}, |
| title = {Social Engineering Specialist: Fine-Tuned Gemma 4 E4B (GGUF)}, |
| year = {2026}, |
| publisher = {Hugging Face}, |
| howpublished = {\url{https://huggingface.co/smd20/socialengineering}}, |
| note = {PhD research release. Advisor: Dr. Amir Nazami Saffa} |
| } |
| ``` |
|
|
| --- |
|
|
| ## Dataset Citations |
|
|
| ```bibtex |
| @misc{sohrab2026seqaen, |
| author = {Sohrab, Samad}, |
| title = {Social Engineering Q&A Dataset (English)}, |
| year = {2026}, |
| howpublished = {\url{https://huggingface.co/datasets/smd20/social-engineering-qa-english}} |
| } |
| |
| @misc{sohrab2026seqafa, |
| author = {Sohrab, Samad}, |
| title = {Social Engineering Q&A Dataset (Persian)}, |
| year = {2026}, |
| howpublished = {\url{https://huggingface.co/datasets/smd20/social-engineering-qa-persian}} |
| } |
| ``` |
|
|
| --- |
|
|
| *Model card last updated: 2026-06-21T12:56:17.859588+00:00* |
|
|
