Gemma 3 4b It GGUF

🚀 Gemma-3-4B-IT Model Introduction

This document provides detailed information about the google/gemma-3-4b-it model, including its base model, license, quantization details, and performance analysis in Turkish language support. It also lists various applications and tools for locally quantized LLMs.

📋 Model Information

🔑 Access Gemma on Hugging Face

To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. Acknowledge license

💾 Quantization Details

Quantized with llama.cpp using all-gguf-same-where

🌍 Turkish Language Support - Performance Analysis

📌 Tested Models

The following local models have been comprehensively tested for their Turkish text generation and understanding capabilities:

1. CohereForAI/c4ai-command-r7b-arabic-02-2025
2. CohereForAI/c4ai-command-r7b-12-2024
3. CohereForAI/aya-expanse-8b
4. ytu-ce-cosmos/Turkish-Llama-8b-Instruct-v0.1
5. google/gemma-3-4b-it
6. google/gemma-3-4b-pt-qat-q4_0-gguf

🔍 Test Criteria

The models were evaluated based on the following 3 basic questions:

1. "Can you explain the 3 most important historical artifacts in Turkey and their cultural significance?"
2. "Compare the differences between the Asian and European sides of Istanbul in English/Turkish"
3. "Prepare a 3-day tourist plan in Antalya"

📊 Notable Results

🔗 Full Test Results

To examine all model responses in detail:
📂 Turkish Supported Model Comparisons

💡 User Guide

- 🔥 **Projects with Turkish Priority** → `Turkish-Llama-8B` (Highest cultural fit)
- 🌐 **Multilingual Applications** → `Aya-Expanse-8B`  
- 📜 **Official Documents** → `Command R7B` series
- 📱 **Low Resource** → `Gemma-3-4B` (Compatible with Android)

⚠️ Important Notes

• All models were tested in the GGUF quantized format.
• There may be a 5-10% error margin in geographical names.
• Ollama/LM Studio is recommended for local use.

🌟 Personal Experience

"In my tests with local models, especially the natural use of Turkish and mastery of cultural context of Turkish-Llama-8B impressed me. You can also try these models in your own projects and share the results on our AnkaNLP page!"

✅ Quantized Models Download List

🔍 Recommended Quantizations

• ✨ General CPU Use: Q4_K_M (Best balance of speed/quality)
• 📱 ARM Devices: Q4_0 (Optimized for ARM CPUs)
• 🏆 Maximum Quality: Q8_0 (Near-original quality)

📦 Full Quantization Options

🚀 Download	🔢 Type	📝 Notes
Download		Basic quantization
Download		Small size
Download		Balanced quality
Download		Better quality
Download		Fast on ARM
Download		Fast, recommended
Download	⭐	Best balance
Download		Good quality
Download		Balanced
Download		High quality
Download	🏆	Very good quality
Download	⚡	Fast, best quality
Download		Maximum accuracy

💡 Tip: Use F16 for maximum precision when quality is critical

🚀 Applications and Tools for Locally Quantized LLMs

🖥️ Desktop Applications

📱 Mobile Applications

🎨 Image Generation Applications