SmolVLM2 2.2B

Runn SmolVLM2 2.2B on Windows: Installation Guide

Anas Mohammad

Feb 25, 2025 • 4 min read

Running SmolVLM2 2.2B on Windows involves several steps, including system requirements, installation of necessary software, and execution of the model.

This article provides a comprehensive guide to help you set up and run the SmolVLM2 model effectively on a Windows operating system.

What is SmolVLM2?

SmolVLM2 is a small yet powerful visual language model that has gained attention for its efficiency and performance. With 2.2 billion parameters, it strikes a balance between computational efficiency and the ability to handle complex tasks.

The model is particularly designed for applications requiring visual understanding combined with language processing, making it suitable for tasks such as image captioning, visual question answering, and more.

System Requirements

Before diving into the installation process, ensure your system meets the following requirements:

Operating System: Windows 10 or later
Processor: Intel Core i5 or equivalent
RAM: At least 16 GB (32 GB recommended)
GPU: NVIDIA GPU with at least 6 GB VRAM (CUDA support is required)
Storage: SSD with at least 20 GB of free space

Installation Steps

1. Install Python

SmolVLM2 requires Python for execution. Follow these steps to install Python:

Download the latest version of Python from the official Python website.
Run the installer and ensure you check the box that says "Add Python to PATH."
Verify the installation by opening Command Prompt and typing:

python --version

2. Install CUDA and cuDNN (for GPU users)

If you plan to run SmolVLM2 using a GPU, install CUDA and cuDNN:

Download the appropriate version of CUDA from the NVIDIA website.
Follow the installation instructions provided on the site.
After installing CUDA, download cuDNN from the NVIDIA cuDNN page (registration required).
Extract the downloaded files and copy them into your CUDA directory (usually C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vX.X).

3. Set Up a Virtual Environment

Using a virtual environment helps manage dependencies effectively:

Open Command Prompt and navigate to your project directory.
Create a virtual environment by running:

python -m venv smolvml_env

Activate the virtual environment:

smolvml_env\Scripts\activate

4. Install Required Libraries

With your virtual environment activated, install the necessary libraries using pip:

pip install torch torchvision torchaudio transformers matplotlib

These libraries are essential for running machine learning models and handling visual data.

5. Download SmolVLM2 Model Files

You can download the SmolVLM2 model files from its official repository or Hugging Face Model Hub. Use Git to clone the repository or download it directly as a ZIP file.

git clone https://huggingface.co/your_model_repository/smolvlm2

6. Set Up Your Project Directory

Create a new directory for your project where you will keep your scripts and model files organized.

7. Write Your Inference Script

Create a new Python script (e.g., run_smolvlm.py) in your project directory with the following code:

import torch
from transformers import SmolVLMModel, SmolVLMProcessor

# Load processor and model
processor = SmolVLMProcessor.from_pretrained("your_model_repository/smolvlm2")
model = SmolVLMModel.from_pretrained("your_model_repository/smolvlm2")

# Prepare input data (image path and text)
image_path = "path/to/your/image.jpg"
text_input = "Describe this image."

# Process inputs
inputs = processor(images=image_path, text=text_input, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)

# Process outputs as needed
print(outputs)

Replace "your_model_repository/smolvlm2" with the actual path where you stored your model files.

8. Run Your Script

With everything set up, run your script from Command Prompt:

python run_smolvlm.py

Ensure that your image path is correct, and you should see output generated based on your input image and text.

Real-World Coding Examples

Example 1: Using Python with Hugging Face Transformers

To run SmolVLM2 2.2B on Windows using Python and the Hugging Face Transformers library, follow these steps:

Load the Model and Run Inference: Use the following Python script to load the SmolVLM2 2.2B model and run inference on an image:PythonCopy

from transformers import AutoProcessor, AutoModelForImageTextToText
import torch

# Load the model and processor
model_path = "HuggingFaceTB/SmolVLM2-2.2B-Instruct"
processor = AutoProcessor.from_pretrained(model_path)
model = AutoModelForImageTextToText.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    _attn_implementation="flash_attention_2"
).to("cuda" if torch.cuda.is_available() else "cpu")

# Load an image
image_path = "path_to_your_image.jpg"
image = Image.open(image_path)

# Prepare inputs
inputs = processor(images=image, return_tensors="pt").to(model.device)

# Generate text
generated_ids = model.generate(**inputs, max_new_tokens=64)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

print(generated_text)

This script loads the SmolVLM2 2.2B model, processes an image, and generates a text description of the image.

Install Dependencies: Ensure you have Python and pip installed. Then, install the necessary dependencies using pip:bashCopy

pip install torch torchvision transformers

Example 2: Using Docker for Isolated Environment

For a more isolated and portable setup, you can run SmolVLM2 2.2B using Docker on Windows. This ensures that all dependencies are contained within the Docker environment.

Install Docker: Download and install Docker Desktop for Windows from the official Docker website.
Access the Web Interface: Open your web browser and navigate to http://localhost:8000 to access the web interface for SmolVLM2.

Pull and Run the Docker Image: Use the following commands to pull and run the Docker image:bashCopy

docker pull mlxcommunity/smolvlm2-2.2b-instruct-mlx
docker run -p 8000:8000 mlxcommunity/smolvlm2-2.2b-instruct-mlx

This will start the SmolVLM2 server inside a Docker container, accessible at http://localhost:8000.

By following these examples, you can effectively run SmolVLM2 2.2B on Windows for various AI-driven tasks, leveraging the power of Hugging Face Transformers and Docker for a seamless experience.

Troubleshooting Common Issues

1. CUDA Errors

If you encounter errors related to CUDA while running your script:

Ensure that your NVIDIA drivers are up to date.
Verify that CUDA is installed correctly by checking its version in Command Prompt:

nvcc --version

2. Memory Issues

If you receive memory-related errors during inference:

Try reducing batch sizes or using smaller images.
Ensure that other applications are not consuming too much GPU memory.

3. Import Errors

If you face issues importing libraries:

Double-check that your virtual environment is activated.
Ensure all required libraries are installed correctly.

Conclusion

Running SmolVLM2 2.2B on Windows can be straightforward if you follow these steps carefully. By ensuring that your system meets the requirements, setting up a proper environment, and writing an efficient inference script, you can leverage this powerful model for various applications in visual language processing.