C4AI Command R7B Arabic is a high-performance language model designed to excel in Arabic and English text processing. Built with a 7-billion parameter transformer architecture and an additional 1 billion embedding parameters, it delivers advanced capabilities for instruction following, retrieval-augmented generation (RAG), and context-aware responses. With a strong focus on linguistic accuracy and cultural understanding, the model effectively handles tasks such as summarization, translation, and text generation while maintaining precise language control. Optimized for enterprises and research applications, C4AI Command R7B Arabic supports a 128K token context length, allowing it to manage long-form text efficiently. Its structured approach to conversational and instruction-based tasks makes it a powerful tool for users seeking a reliable and adaptable language model.
Model Performance
C4AI Command R7B Arabic excels on standardized and externally verifiable Arabic language benchmarks such as AlGhafa-Native, Arabic MMLU, instruction following (IFEval Arabic), and RAG (TyDi QA Arabic and FaithEval Arabic*).
| Model | C4AI Command R7B Arabic | Command R7B | Gemma 9B | Llama 3.1 8B | Qwen 2.5 7B | Ministral 8B |
|---|
| Average | 69.3 | 65.8 | 67.0 | 58.4 | 62.9 | 52.5 |
| AlGhafa-Native | 82.2 | 81.5 | 81.3 | 80.1 | 80.2 | 76.6 |
| Arabic MMLU | 60.9 | 59.7 | 62.4 | 56.6 | 61.2 | 53.6 |
| IFEval AR | 69.0 | 57.8 | 67.8 | 48.4 | 62.4 | 49.3 |
| TyDI QA Arabic | 83.0 | 79.9 | 76.4 | 65.9 | 60.9 | 57.7 |
| FaithEval Arabic* | 51.6 | 49.9 | 47.0 | 40.9 | 49.9 | 25.5 |
Model Resource
Hugging Face
Link: https://huggingface.co/CohereForAI/c4ai-command-r7b-arabic-02-2025
Ollama
Link: https://ollama.com/library/command-r7b-arabic:7b
Prerequisites for Installing Command R7B Arabic Model Locally
- GPU:
- Memory (VRAM):
- Minimum: 16GB (with 8-bit or 4-bit quantization).
- Recommended: 24GB for smoother execution.
- Optimal: 48GB for full performance at FP16 precision.
- Type: NVIDIA GPUs with Tensor Cores (e.g., RTX 4090, A6000, A100, H100).
- Disk Space:
- Minimum: 40GB free SSD storage.
- Recommended: 100GB SSD for storing additional checkpoints, logs, and datasets.
- RAM:
- Minimum: 24GB.
- Recommended: 48GB for smoother operation, especially with large datasets.
- CPU:
- Minimum: 16 cores.
- Recommended: 24-48 cores for fast data preprocessing and I/O operations.
Step-by-Step Process to Install Command R7B Arabic Model Locally
For the purpose of this tutorial, we will use a GPU-powered Virtual Machine offered by NodeShift; however, you can replicate the same steps with any other cloud provider of your choice. NodeShift provides the most affordable Virtual Machines at a scale that meets GDPR, SOC2, and ISO27001 requirements.
Step 1: Sign Up and Set Up a NodeShift Cloud Account
Visit the NodeShift Platform and create an account. Once you’ve signed up, log into your account.
Follow the account setup process and provide the necessary details and information.
Step 2: Create a GPU Node (Virtual Machine)
GPU Nodes are NodeShift’s GPU Virtual Machines, on-demand resources equipped with diverse GPUs ranging from H100s to A100s. These GPU-powered VMs provide enhanced environmental control, allowing configuration adjustments for GPUs, CPUs, RAM, and Storage based on specific requirements.
Navigate to the menu on the left side. Select the GPU Nodes option, create a GPU Node in the Dashboard, click the Create GPU Node button, and create your first Virtual Machine deployment.
Step 3: Select a Model, Region, and Storage
In the “GPU Nodes” tab, select a GPU Model and Storage according to your needs and the geographical region where you want to launch your model.
We will use 1x RTX A6000 GPU for this tutorial to achieve the fastest performance. However, you can choose a more affordable GPU with less VRAM if that better suits your requirements.
Step 4: Select Authentication Method
There are two authentication methods available: Password and SSH Key. SSH keys are a more secure option. To create them, please refer to our official documentation.
Step 5: Choose an Image
Next, you will need to choose an image for your Virtual Machine. We will deploy Command R7B Arabic on an NVIDIA Cuda Virtual Machine. This proprietary, closed-source parallel computing platform will allow you to install Command R7B Arabic on your GPU Node.
After choosing the image, click the ‘Create’ button, and your Virtual Machine will be deployed.
Step 6: Virtual Machine Successfully Deployed
You will get visual confirmation that your node is up and running.
Step 7: Connect to GPUs using SSH
NodeShift GPUs can be connected to and controlled through a terminal using the SSH key provided during GPU creation.
Once your GPU Node deployment is successfully created and has reached the ‘RUNNING’ status, you can navigate to the page of your GPU Deployment Instance. Then, click the ‘Connect’ button in the top right corner.
Now open your terminal and paste the proxy SSH IP or direct SSH IP.
Next, if you want to check the GPU details, run the command below:
nvidia-smi
Step 8: Install Ollama
After connecting to the terminal via SSH, it’s now time to install Ollama from the official Ollama website.
Website Link: https://ollama.com/
Run the following command to install the Ollama:
curl -fsSL https://ollama.com/install.sh | sh
Step 9: Serve Ollama
Run the following command to host the Ollama so that it can be accessed and utilized efficiently:
ollama serve
Now, “Ollama is running.”
Step 10: Select Command R7B Arabic Model
Link: https://ollama.com/library/command-r7b-arabic:7b
Command R7B Arabic model is available in only one size: 7B. We will run it on our GPU virtual machine.
Step 11: Connect with SSH
Now, open a new tab in the terminal and reconnect using SSH.
Step 12: Check Commands
Run, the following command to see a list of available commands:
ollama
Step 13: Check Available Models
Run the following command to check if the downloaded model are available:
ollama list
Step 14: Pull Command R7B Arabic Model
Run the following command to pull the Command R7B Arabic model:
ollama pull command-r7b-arabic:7b
Step 15: Run Command R7B Arabic Model
Now, you can run the model in the terminal using the following command and interact with your model:
ollama run command-r7b-arabic:7b
Note: This is a step-by-step guide for interacting with your model. It covers the first method for installing Command R7B Arabic locally using Ollama and running it in the terminal.
Option 1: Using Ollama (Terminal)
- Install Ollama: Download and install the Ollama tool from the official site.
- Pull the Model: Run the following command to download the desired model:
ollama pull command-r7b-arabic:7b
- Run the Model: Start the model in the terminal:
ollama run command-r7b-arabic:7b
Option 2: Using Open WebUI
- Set Up Open WebUI:
Follow our Open WebUI Setup Guide to configure the interface. Ensure all dependencies are installed and the environment is correctly set up.
- Refresh the Interface:
Confirm that the Command R7B Arabic model has been downloaded and is visible in the list of available models on the Open WebUI.
- Select Your Model:
Choose the Command R7B Arabic model from the list. This model is available in a single size.
- Start Interaction:
Begin using the model by entering your queries in the interface. The Command R7B Arabic is designed for high-quality instruction-based interactions, so input clear and detailed queries for the best results.
Option 3: Using Hugging Face and Jupyter Notebook
- Follow our Jupyter Notebook Setup Guide to configure your notebook environment. Ensure that all required dependencies are installed and that your Jupyter Notebook is set up correctly for optimal use.
When choosing an image for your Virtual Machine, select the Jupyter Notebook image. This open-source platform allows you to install and run the Command R7B Arabic on your GPU node. By running this model on a Jupyter Notebook, you can avoid using the terminal, simplifying the process and reducing setup time. This approach enables you to configure the model in just a few steps and minutes.
Note: NodeShift provides multiple image template options, such as TensorFlow, PyTorch, NVIDIA CUDA, Deepo, Whisper ASR Webservice, and Jupyter Notebook. With these options, you don’t need to install additional libraries or packages to run Jupyter Notebook. You can start Jupyter Notebook in just a few simple clicks.
- Access model from Hugging Face:
Link: https://huggingface.co/CohereForAI/c4ai-command-r7b-arabic-02-2025
You need to agree to share your contact information to access this model. Fill in all the mandatory details, such as your name and affiliation, and then wait for approval from Hugging Face and CohereForAI to gain access and use the model.
You will be granted access to this model within an minute, provided you have filled in all the details correctly.
GPU Recommendations
| GPU Model | VRAM | Ideal Use Case |
|---|
| RTX 3090 | 24GB | Suitable for quantized 8-bit/4-bit inference. |
| RTX 4090 | 24GB | Excellent for inference and lightweight tasks. |
| A6000 | 48GB | Optimal for FP16 inference and fine-tuning. |
| A100 (40GB) | 40GB | Great for general training and inference. |
| A100 (80GB) | 80GB | Ideal for extended context length (128k). |
| H100 (80GB) | 80GB | Best for large-scale fine-tuning and RAG tasks. |
System Configuration Summary
After setting up the VM and running your Jupyter Notebook, start installing the Command R7B model.
Step 1: Check GPU Availability
First, confirm that your Jupyter Notebook is correctly using the GPU.
Run the following command in a Jupyter cell:
!nvidia-smi
If the output shows details of your NVIDIA GPU, you are good to proceed.
Step 2: Install Dependencies
Ensure your environment has the required libraries.
Run the following commands in a Jupyter cell:
!pip install --upgrade pip
!pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
!pip install transformers accelerate safetensors sentencepiece
Step 3: Install Hugging Face Transformers
Since the model requires a custom tokenizer and additional configurations, install the Hugging Face Transformers library directly from the source.
!pip install 'git+https://github.com/huggingface/transformers.git'
Step 4: Load the Model in Jupyter Notebook
Now, load the C4AI Command R7B Arabic model.
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# Define model path
model_id = "CohereForAI/c4ai-command-r7b-arabic-02-2025"
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Load model with GPU optimization
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16, # Use FP16 to reduce memory usage
device_map="auto", # Automatically assigns to available GPUs
)
# Check GPU memory usage
print(torch.cuda.memory_summary(device="cuda"))
Step 5: Generate Text Using the Model
Now, let’s test the model by generating text in Arabic and English.
def generate_response(prompt):
inputs = tokenizer(prompt, return_tensors="pt").to("cuda") # Move inputs to GPU
output = model.generate(
**inputs,
max_new_tokens=150, # Limit response length
temperature=0.7, # Controls randomness (lower = more deterministic)
do_sample=True, # Enables sampling for diverse outputs
pad_token_id=tokenizer.pad_token_id
)
return tokenizer.decode(output[0], skip_special_tokens=True)
# Run Arabic & English prompts
arabic_prompt = "ما هي عاصمة الإمارات العربية المتحدة؟"
english_prompt = "What is the capital of the United Arab Emirates?"
# Generate and print responses
print("🔹 Arabic:", generate_response(arabic_prompt))
print("🔹 English:", generate_response(english_prompt))
Step 6: If You Encounter GPU Memory Issues
If your GPU has low VRAM, try offloading some parts of the model to the CPU.
Modify the model loading command as follows:
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map={"": "cuda:0"}, # Forces primary GPU
offload_folder="./offload" # Offloads excess parameters to CPU
)
If needed, use bitsandbytes to quantize the model to 4-bit for lower VRAM usage:
!pip install bitsandbytes accelerate
from transformers import BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(
load_in_4bit=True, # Enables 4-bit quantization
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True
)
model = AutoModelForCausalLM.from_pretrained(
model_id,
quantization_config=quantization_config,
device_map="auto"
)
Step 7: Deploy an Interactive Chatbot (Optional)
If you want an interactive chatbot in Jupyter Notebook, use Gradio.
!pip install gradio
import gradio as gr
def chatbot_response(prompt):
return generate_response(prompt)
gr.Interface(
fn=chatbot_response,
inputs=gr.Textbox(lines=2, placeholder="Type your message..."),
outputs="text",
title="C4AI Command R7B Arabic Chatbot",
live=True,
).launch(share=True)
Once executed, this will provide a Gradio UI link where you can interact with the model.
By following these steps, you can successfully set up, run, and interact with the C4AI Command R7B Arabic model in your Jupyter Notebook. Whether you use it for text generation, research, or chatbot development, this guide ensures a seamless deployment on your GPU-powered environment.
Conclusion
The C4AI Command R7B Arabic model is a robust solution for advanced text processing in Arabic and English. With a 7-billion parameter transformer architecture and 1 billion embedding parameters, it excels in tasks such as instruction following, retrieval-augmented generation (RAG), translation, and contextual understanding. Its ability to handle 128K tokens ensures efficient processing of long-form content, making it suitable for enterprise applications, research, and multilingual interactions.
By following the step-by-step installation guide, users can easily set up the model on NodeShift Cloud, Ollama, Open WebUI, or Jupyter Notebook, ensuring flexibility across different platforms. Whether deployed for chat applications, content generation, or research, C4AI Command R7B Arabic delivers precise language control and structured responses.
