GOVINDFROM/aegis-gemma-4-e4b-it-scam-defense-ollama-gguf-v1

AEGIS Gemma 4 E4B IT Scam Defense Ollama GGUF v1

AEGIS is a locally deployable Gemma 4 E4B instruction-tuned model package for scam-defense analysis. It is tuned for one job: analyze a phone-call transcript for epistemic manipulation and return structured JSON describing how a scammer is trying to distort the target's decision-making process.

This repository is the Ollama / GGUF release package for:

• —Local inference with Ollama
• —Offline demos and hackathon judging
• —Safety and trust evaluation on scam-call transcripts

The package is designed for the GOVINDFROM/aegis-gemma-4-e4b-it-scam-defense-ollama-gguf-v1 Hugging Face repo.

What This Model Does

Given a conversation transcript, AEGIS scores six manipulation vectors:

1. 1.belief_installation
2. 2.verification_suppression
3. 3.urgency_fabrication
4. 4.authority_hijacking
5. 5.emotional_flooding
6. 6.exit_path_closure

It returns:

• —Per-vector scores from 0-100
• —integrity_score
• —risk_level
• —A plain-language explanation
• —A recommended next action
• —Evidence snippets per vector

Files

• —gemma-4-e4b-it.Q4_K_M.gguf: quantized GGUF model for local inference
• —Modelfile: Ollama model definition with the AEGIS system behavior and prompt template
• —README.md: usage and model card

Base Model

• —Base model: google/gemma-4-E4B-it
• —Deployment format: GGUF
• —Quantization: Q4_K_M

Training Summary

This release is derived from a fine-tuned Gemma 4 E4B instruction model adapted for scam-defense transcript analysis.

• —Training method: LoRA
• —LoRA rank: 16
• —Learning rate: 2e-4
• —Epochs: 3
• —Train size: 396
• —Eval size: 44
• —Max sequence length: 2048
• —Final reported training loss: 0.4065
• —Training hardware: NVIDIA A100-SXM4-80GB

Recommended Use

Use this model to:

• —Analyze scam and non-scam phone-call transcripts
• —Demonstrate local-first AI safety tooling
• —Power an Ollama-based Streamlit, Gradio, or desktop app
• —Support structured risk scoring for research demos

Do not use this model as:

• —A legal authority
• —A replacement for emergency services
• —A sole decision-maker in high-stakes financial or criminal matters

Download And Run With Ollama

Option 1: Download from Hugging Face, then create the Ollama model

1. 1.Download the repository:

git lfs install
git clone https://huggingface.co/GOVINDFROM/aegis-gemma-4-e4b-it-scam-defense-ollama-gguf-v1
cd aegis-gemma-4-e4b-it-scam-defense-ollama-gguf-v1

1. 1.Create the local Ollama model:

ollama create aegis -f Modelfile

1. 1.Run it:

ollama run aegis

Option 2: Download with the Hugging Face CLI

huggingface-cli download GOVINDFROM/aegis-gemma-4-e4b-it-scam-defense-ollama-gguf-v1 --local-dir .
ollama create aegis -f Modelfile
ollama run aegis

Example Prompt

Paste a transcript like this into Ollama:

Analyze this phone call for epistemic manipulation:

Caller: Hello, this is Agent Williams from the IRS Criminal Investigation Division.
We found major discrepancies in your tax filings.
A federal arrest warrant has been issued in your name.
You must pay immediately via gift cards to stop enforcement.
Do not hang up or contact anyone else.

Expected behavior:

• —High authority_hijacking
• —High urgency_fabrication
• —High verification_suppression
• —risk_level near CRITICAL
• —Very low integrity_score

Example Output Shape

The model is configured to emit JSON in this structure:

{
  "manipulation_vectors": {
    "belief_installation": 95,
    "verification_suppression": 90,
    "urgency_fabrication": 100,
    "authority_hijacking": 100,
    "emotional_flooding": 85,
    "exit_path_closure": 95
  },
  "integrity_score": 5,
  "risk_level": "CRITICAL",
  "explanation": "The caller is using false authority, panic, and isolation tactics to block independent verification.",
  "recommended_action": "Terminate the call and verify the claim through an official number you find independently.",
  "evidence": {
    "belief_installation": "False claim of tax discrepancies and an arrest warrant.",
    "verification_suppression": "Instructs the target not to contact anyone else.",
    "urgency_fabrication": "Immediate payment demanded.",
    "authority_hijacking": "Claims to be from the IRS Criminal Investigation Division.",
    "emotional_flooding": "Threat of arrest creates fear.",
    "exit_path_closure": "Explicit instruction not to hang up."
  }
}

Hardware Notes

• —Runs locally with Ollama
• —Suitable for laptops / desktops
• —Quantized package size is about 5.3 GB
• —GPU helps, but CPU inference is possible with higher latency

Limitations

• —This model analyzes transcript text, not verified ground truth
• —Scam tactics evolve and can differ by region and language
• —Structured JSON output may occasionally need retry logic in downstream apps
• —Scores should support human judgment, not replace it

Safety Notes

This model is intended to help users recognize manipulative scam behavior. It should be deployed with:

• —Clear user-facing disclaimers
• —Human override
• —Independent verification guidance
• —Privacy-preserving local inference where possible

Citation

If you use this model, cite the project repository and hackathon submission:

@misc{aegis_gemma4_2026,
  title={AEGIS: Adaptive Epistemic Guard for Intelligent Scam Defense},
  author={GOVINDFROM},
  year={2026},
  howpublished={Hugging Face model repository}
}