Instructions to use matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF", dtype="auto")

llama-cpp-python

How to use matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF",
	filename="C4AI-Command-R7B-Arabic-02-2025-f16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF:Q4_K_M

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 matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF:Q4_K_M

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 matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF:Q4_K_M

Use Docker

docker model run hf.co/matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF with Ollama:
```
ollama run hf.co/matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF:Q4_K_M
```

Unsloth Studio

How to use matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF 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 matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF 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 matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF to start chatting

How to use matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF:Q4_K_M

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": "matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF:Q4_K_M

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 matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF with Docker Model Runner:
```
docker model run hf.co/matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF:Q4_K_M
```

Lemonade

How to use matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.c4ai-command-r7b-arabic-02-2025-GGUF-Q4_K_M

List all available models

lemonade list

Base model: CohereForAI/c4ai-command-r7b-arabic-02-2025
License: CC-BY-NC-4.0

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

🌍 Türkçe Dil Destekli Modeller - Performans Analizi

📌 Test Edilen Modeller

Aşağıdaki yerel modeller, Türkçe metin üretimi ve anlama yetenekleri açısından kapsamlı şekilde test edilmiştir:

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

🔍 Test Kriterleri

Modeller şu 3 temel soruyla değerlendirilmiştir:

1. "Türkiye'deki en önemli 3 tarihi eseri ve kültürel önemini açıklar mısın?"
2. "İstanbul'un Asya-Avrupa yakası farklarını İngilizce/Türkçe karşılaştır"
3. "Antalya'da 3 günlük turist planı hazırla"

📊 Öne Çıkan Sonuçlar

Model	Kültürel Doğruluk	Dil Akıcılığı	Çok Dillilik	En Güçlü Yönü
Turkish-Llama-8B	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐	Metaforik anlatım ve yerel bağlam
Aya-Expanse-8B	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	20+ dil desteği
Command R7B Arabic	⭐⭐⭐	⭐⭐⭐	⭐⭐⭐	Arapça-Türkçe çeviri

🔗 Tam Test Sonuçları

Tüm model yanıtlarını detaylı incelemek için:
📂 Türkçe Destekli Model Karşılaştırmaları

💡 Kullanıcı Rehberi

- 🔥 **Türkçe Öncelikli Projeler** → `Turkish-Llama-8B` (En yüksek kültürel uyum)
- 🌐 **Çok Dilli Uygulamalar** → `Aya-Expanse-8B`  
- 📜 **Resmi Dokümanlar** → `Command R7B` serisi
- 📱 **Düşük Kaynak** → `Gemma-3-4B` (Android uyumlu)

⚠️ Önemli Notlar

Tüm modeller GGUF kuantize formatında test edilmiştir
Coğrafi isimlerde %5-10 hata payı olabilir
Yerel kullanım için Ollama/LM Studio önerilir

🌟 Kişisel Deneyim

"Yerel modellerle yaptığım testlerde özellikle Turkish-Llama-8B'nin Türkçe'yi doğal kullanımı ve kültürel bağlam hakimiyeti beni etkiledi. Siz de kendi projelerinizde bu modelleri deneyerek sonuçları AnkaNLP sayfamızda paylaşabilirsiniz!"

✅ 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	$Q2\_K$	Basic quantization
Download	$Q3\_K\_S$	Small size
Download	$Q3\_K\_M$	Balanced quality
Download	$Q3\_K\_L$	Better quality
Download	$Q4\_0$	Fast on ARM
Download	$Q4\_K\_S$	Fast, recommended
Download	$Q4\_K\_M$ ⭐	Best balance
Download	$Q5\_0$	Good quality
Download	$Q5\_K\_S$	Balanced
Download	$Q5\_K\_M$	High quality
Download	$Q6\_K$ 🏆	Very good quality
Download	$Q8\_0$ ⚡	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

Application	Description	Download Link
Llama.cpp	A fast and efficient inference engine for GGUF models.	GitHub Repository
Ollama	A streamlined solution for running LLMs locally.	Website
AnythingLLM	An AI-powered knowledge management tool.	GitHub Repository
Open WebUI	A user-friendly web interface for running local LLMs.	GitHub Repository
GPT4All	A user-friendly desktop application supporting various LLMs, compatible with GGUF models.	GitHub Repository
LM Studio	A desktop application designed to run and manage local LLMs, supporting GGUF format.	Website
GPT4All Chat	A chat application compatible with GGUF models for local, offline interactions.	GitHub Repository

📱 Mobile Applications

Application	Description	Download Link
ChatterUI	A simple and lightweight LLM app for mobile devices.	GitHub Repository
Maid	Mobile Artificial Intelligence Distribution for running AI models on mobile devices.	GitHub Repository
PocketPal AI	A mobile AI assistant powered by local models.	GitHub Repository
Layla	A flexible platform for running various AI models on mobile devices.	Website

🎨 Image Generation Applications

Application	Description	Download Link
Stable Diffusion	An open-source AI model for generating images from text.	GitHub Repository
Stable Diffusion WebUI	A web application providing access to Stable Diffusion models via a browser interface.	GitHub Repository
Local Dream	Android Stable Diffusion with Snapdragon NPU acceleration. Also supports CPU inference.	GitHub Repository
Stable-Diffusion-Android (SDAI)	An open-source AI art application for Android devices, enabling digital art creation.	GitHub Repository

Downloads last month: 758

GGUF

Model size

8B params

Architecture

cohere2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

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Model tree for matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF

Base model

CohereLabs/c4ai-command-r7b-arabic-02-2025

Quantized

(9)

this model

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Favori

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Türkçe

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