Instructions to use smd20/socialengineering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use smd20/socialengineering with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="smd20/socialengineering",
	filename="unsloth-gemma-4-E4B-it.BF16-mmproj.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use smd20/socialengineering with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf smd20/socialengineering:BF16
# Run inference directly in the terminal:
llama cli -hf smd20/socialengineering:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf smd20/socialengineering:BF16
# Run inference directly in the terminal:
llama cli -hf smd20/socialengineering:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf smd20/socialengineering:BF16
# Run inference directly in the terminal:
./llama-cli -hf smd20/socialengineering:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf smd20/socialengineering:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf smd20/socialengineering:BF16

Use Docker

docker model run hf.co/smd20/socialengineering:BF16

LM Studio
Jan

vLLM

How to use smd20/socialengineering with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "smd20/socialengineering"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "smd20/socialengineering",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/smd20/socialengineering:BF16

Ollama
How to use smd20/socialengineering with Ollama:
```
ollama run hf.co/smd20/socialengineering:BF16
```

Unsloth Studio

How to use smd20/socialengineering 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 smd20/socialengineering 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 smd20/socialengineering to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for smd20/socialengineering to start chatting

How to use smd20/socialengineering with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf smd20/socialengineering:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "smd20/socialengineering:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use smd20/socialengineering with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf smd20/socialengineering:BF16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default smd20/socialengineering:BF16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use smd20/socialengineering with Docker Model Runner:
```
docker model run hf.co/smd20/socialengineering:BF16
```

Lemonade

How to use smd20/socialengineering with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull smd20/socialengineering:BF16

Run and chat with the model

lemonade run user.socialengineering-BF16

List all available models

lemonade list

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 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
Domain	Social engineering, phishing, red-team awareness
Languages	English, Persian (Farsi)
Context length (training)	2,048 tokens
Repository	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	3,330
Persian Q&A	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

Controlled segmentation of reference books
Schema-driven knowledge article generation (JSONL)
Grounded bilingual Q&A generation with strict source constraints
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)

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

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

huggingface-cli download smd20/socialengineering \
  unsloth-gemma-4-E4B-it.BF16.gguf \
  unsloth-gemma-4-E4B-it.BF16-mmproj.gguf

Inference Examples

`llama.cpp`

llama-cli -hf smd20/socialengineering:BF16 --jinja

For multimodal usage:

llama-mtmd-cli -hf smd20/socialengineering:BF16 --jinja

`llama-cpp-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

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 for efficient Gemma 4 fine-tuning and GGUF export.

Citation

If you use this model or the associated datasets in academic work, please cite:

@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

@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

Downloads last month: 464

GGUF

Model size

8B params

Architecture

gemma4

Hardware compatibility

16-bit

smd20
/

socialengineering

Social Engineering Specialist — Gemma 4 E4B (GGUF)

Model Summary

Intended Use

Primary use cases

Out-of-scope / misuse

Training Procedure

Training hyperparameters

Export configuration

Training Data

Reference corpora

Corpus construction pipeline

Training Corpus Overview

Evaluation & Limitations

How to Download from Hugging Face

Option 1 — `huggingface_hub` (recommended)

Option 2 — Snapshot download

Option 3 — CLI

Inference Examples

`llama.cpp`

`llama-cpp-python`

Ollama

Authorship, Ownership, and Legal Notice

Acknowledgments

Citation

Dataset Citations

Datasets used to train smd20/socialengineering

Social Engineering Specialist — Gemma 4 E4B (GGUF)

Model Summary

Intended Use

Primary use cases

Out-of-scope / misuse

Training Procedure

Training hyperparameters

Export configuration

Training Data

Reference corpora

Corpus construction pipeline

Training Corpus Overview

Evaluation & Limitations

How to Download from Hugging Face

Option 1 — huggingface_hub (recommended)

Option 2 — Snapshot download

Option 3 — CLI

Inference Examples

llama.cpp

llama-cpp-python

Ollama

Authorship, Ownership, and Legal Notice

Acknowledgments

Citation

Dataset Citations

Datasets used to train smd20/socialengineering

Option 1 — `huggingface_hub` (recommended)

`llama.cpp`

`llama-cpp-python`