SmolVLM2: Bringing Video Understanding to Every Device

 

What if your phone interpret the videos you watch? Imagine a cloud-free AI that evaluates actions, emphasizes significant moments, and creates relevant explanations. This is what SmolVLM2 offers. 

Video comprehension was originally intended for huge AI models with high-end GPUs and plenty of processing resources. With SmolVLM2, that changes. Video intelligence is now available on powerful cloud servers, laptops, and mobile devices with this new family of products. 

Why is SmolVLM2 special? And what makes it a video AI game-changer? More importantly, how can you use it now? Let's discuss it below:

 

Why SmolVLM2 Matters

Let's face it, most video AI models are heavy. They take time to load, cost a lot in cloud resources, and are unaffordable for most users. SmolVLM2 reverses everything. Compact yet powerful, it runs well on limited-power devices.

SmolVLM2 runs across environments, unlike previous models that need enormous infrastructure. Researchers, developers, and AI fanatics may now afford cutting-edge video understanding.

The best part: Three sizes of SmolVLM2 are available:

• SmolVLM2-2.2B: Powerful for demanding video and vision applications.
• SmolVLM2-500M: This small variant offers remarkable results with little computing requirements.
• SmolVLM2-256M: Smallest, designed for ultra-lightweight applications.

You get quick inference, efficient memory use, and high-quality video interpretation with any version.

Getting Started with SmolVLM2

Setting up SmolVLM2 is insanely simple. Those familiar with Hugging Face's transformers library will feel at home.

Let's install it first:

pip install git+https://github.com/huggingface/transformers@v4.49.0-Sm

After installation, loading the model is easy:

from transformers import AutoProcessor, AutoModelForImageTextToText
import torch

processor = AutoProcessor.from_pretrained("model_path")
model = AutoModelForImageTextToText.from_pretrained(
   "model_path",
   torch_dtype=torch.bfloat16
).to("cuda")

And just like that, we're ready to process videos!

Running Video Inference with Transformers

Learn how SmolVLM2 understands videos. Imagine you want AI to describe a video file. That is easy using SmolVLM2:

messages = [
    {
       "role": "user",
       "content": [
           {"type": "video", "path": "video.mp4"},
           {"type": "text", "text": "Describe this video"}
        ]
    }
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
   return_tensors="pt"
).to(model.device)

generated_ids = model.generate(**inputs)
print(processor.batch_decode(generated_ids, skip_special_tokens=True)[0])

The AI will describe the video in text within seconds. There are no hard-to-understand setups or GPU needs; just simple video intelligence whenever you need it. 

Beyond Video: Multi-Image Inference 

SmolVLM2 understands conversations with many images as well as videos. Let AI compare two images and then explain the differences. Let's see h ow it does this: 

messages = [
    {
       "role": "user",
       "content": [
           {"type": "image", "url": "image1.jpg"},
           {"type": "image", "url": "image2.jpg"},
           {"type": "text", "text": "Compare these images"}
        ]
    }
]

You can receive informative AI-generated picture comparisons in a few lines of code.

SmolVLM2 with MLX (for Apple Silicon Users)

You will be happy to know that SmolVLM2 works great with MLX, Apple's AI system for better macOS speed if you have an M1/M2 Mac. Set it up:

pip install git+https://github.com/pcuenca/mlx-vlm.git@smolvlm

Running inference is as simple as this:

python -m mlx_vlm.generate --model mlx-community/SmolVLM2-500M-Video-Instruct-mlx --image "image.jpg" --prompt "Describe this image"

Boom! Your Mac now has AI-powered image understanding that is quick, powerful, and efficient.

Fine-Tuning SmolVLM2 for Custom Data

Want to customize SmolVLM2 for your dataset? With Hugging Face Transformers on video-caption pairings, you may adjust it. You can teach SmolVLM2 to get better results using a dataset of described videos.

Google Colab allows fine-tuning, making it available to anybody online.

Real-World Applications of SmolVLM2

Where to use SmolVLM2? The possibilities are unlimited, but here are some fascinating real-world applications:

1. iPhone Video Analysis: SmolVLM2 allows real-time video analysis on iPhones without cloud reliance.
2. VLC Media Player Integration: SmolVLM2 integrates with VLC for AI-powered video navigation and scene descriptions.
3. AI Highlight Generator: Detects and summarizes key moments in lengthy recordings, ideal for sports, meetings, and more.

The fact that SmolVLM2 works in these various situations shows its innovation.

Conclusion

SmolVLM2 is a video understanding conceptual change, not simply another AI model. Finally, we have an AI that is small enough to run on any device and strong enough to compete with huge cloud-based models in terms of performance.

This is your opportunity to discover the future of video intelligence, whether you are a developer, researcher, or AI enthusiast. Try SmolVLM2 now to change how we watch videos.