Gemma 3: NEW Opensource Multimodal Model Beats DeepSeek V3 & o3 Mini! (Fully Tested)

Google has introduced the most capable model named Gamma 3, optimized for running on a single GPU or TPU. This collection of lightweight open AI models supports multiple devices and languages with a focus on efficiency and performance.

Gamma 3 models are designed for efficiency, offering four models with varying parameters pre-trained in 140+ languages. They support multimodal infused models and are optimized for different devices, providing high performance compared to larger models.

You can install the Gamma 3 model locally using Google's dodev website, making it easily accessible. The installation process involves copying the command prompt and selecting the desired model version. The system is user-friendly and supports easy deployment on different platforms.

The model showcases the ability to generate structured functional code efficiently, evidenced by a detailed expense tracker app. The app allows for transaction tracking, adding expenses, and provides a financial summary with high accuracy.

The evaluation focuses on the model's image analysis performance, demonstrating its ability to create stories based on provided images. Although it succeeded in some tasks, it showed limitations in certain symmetrical image generation tasks.

The model was tested on a mathematical problem-solving prompt, successfully solving algebraic equations. It displayed proficiency in solving mathematical problems accurately.

The model demonstrated logical reasoning and deduction abilities by accurately solving a logical problem involving cows and chickens. It showcased effective logic application and reached the correct solution.

The model was evaluated on a Python programming prompt involving the sum of even numbers in a list. Despite the challenge of introducing an odd number, it managed to handle the task effectively and demonstrate good programming skills.

In a common sense reasoning prompt, the model showcased knowledge and reasoning skills by explaining the outcome of a scenario involving melting sensations and molecular processes. It successfully provided a logical explanation.

Despite facing challenges in some prompts, the model displayed impressive performance in various domains. While it may lack in coding, its proficiency in math, logic, and common sense reasoning makes it a powerful and capable model for diverse tasks.