File size: 2,596 Bytes
aca1225 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 | ---
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>" |