---
license: gemma
base_model: unsloth/gemma-3-4b-it
tags:
- gguf
- gemma
- finetuned
- affiliate
- screening
- conversational
- ollama
- llama-cpp
- unsloth
- q4_k_m
language:
- en
- bn
pipeline_tag: text-generation
---

# 🤖 AI Manager - Affiliate Screening Assistant

## 📌 Overview

**AI Manager** is a fine-tuned version of Google's **Gemma-3-4B** model, specifically trained for **affiliate application screening**. It acts as an intelligent assistant that verifies affiliate applications, enforces network policies, and communicates in **English, Bangla, or Banglish** based on user preference.

This model is designed for **Monir Hasan (Monir iMonetizeIt)**, Sales Manager & Affiliate Specialist at **iMonetizeIt**, a global CPA and Smartlink affiliate network.

---

## 🎯 Use Cases

- ✅ **Affiliate Application Screening** – Verify applicant eligibility
- ✅ **Policy Enforcement** – Ensure compliance with network rules
- ✅ **Document Verification** – Check NID, screenshots, and other documents
- ✅ **Multi-language Support** – Respond in English, Bangla, or Banglish
- ✅ **Rule-based Decision Making** – Enforce 18+ age limit, desktop screenshots, etc.

---

## 🛠️ Training Details

| Parameter | Value |
|-----------|-------|
| **Base Model** | `unsloth/gemma-3-4b-it` |
| **Fine-tuning Method** | QLoRA (4-bit quantization) |
| **LoRA Rank (r)** | 32 |
| **LoRA Alpha** | 64 |
| **Trainable Parameters** | 65.5M (1.5% of total) |
| **Dataset Size** | 584 training examples |
| **Evaluation Size** | 64 validation examples |
| **Epochs** | 4 |
| **Batch Size** | 2 |
| **Learning Rate** | 2e-4 |
| **Optimizer** | AdamW 8-bit |
| **Loss Function** | Cross-entropy with response-only masking |
| **Hardware** | Kaggle T4 GPU (2x) |

### Training Progress

| Epoch | Training Loss | Validation Loss |
|-------|---------------|-----------------|
| 1 | 2.439 | 1.902 |
| 2 | 0.709 | 1.683 |
| 3 | 0.453 | 1.695 |
| 4 | 0.385 | 1.702 |

---

## 📁 Model Files

| File | Size | Description |
|------|------|-------------|
| `gemma-3-4b-it.Q4_K_M.gguf` | 2.49 GB | Quantized GGUF model (Q4_K_M) |
| `gemma-3-4b-it.F16-mmproj.gguf` | 812 MB | Multimodal projection file |

---

## 🚀 How to Use

### Option 1: Ollama (Recommended)

Create a `Modelfile`:

```dockerfile
FROM https://huggingface.co/imonetizeitbd/ai-manager/resolve/main/gemma-3-4b-it.Q4_K_M.gguf

TEMPLATE """<bos><start_of_turn>user
{{ .Prompt }}<end_of_turn>
<start_of_turn>model
{{ .Response }}<end_of_turn>"""

PARAMETER temperature 0.3
PARAMETER top_p 0.9
PARAMETER stop "<end_of_turn>"