The video introduces the new Gemma 3 family of models, featuring four distinct versions with enhanced multimodal capabilities, longer context handling, and improved training data, making them suitable for various tasks like image analysis and text generation. It emphasizes the ease of implementation using the Transformers library and highlights the strong performance of the 12B model for local deployment.
The video discusses the release of the new Gemma 3 family of models, which marks a significant update since the introduction of the original Gemma models earlier in 2024. Unlike the previous versions, which had limited models, Gemma 3 introduces four distinct models: a 1B model, a 4B model, a 12B model, and a large 27B model. This release also includes both base models and instruction fine-tuned models, allowing users to conduct their own fine-tuning and research, addressing a previous concern where base models were not made available.
One of the standout features of the Gemma 3 models is their multimodal capability, allowing the 4B, 12B, and 27B models to process both text and images. This is achieved through a modified encoder, enabling tasks such as visual question answering and image analysis. The 1B model, however, does not support this feature. Additionally, the models have been trained to handle a significantly longer context, with the 1B model supporting 32,000 tokens and the others supporting 128,000 tokens, which is a substantial improvement over previous iterations.
The training data for the Gemma 3 models has also been enhanced, with the amount of multilingual data used being approximately double that of the Gemma 2 models. Each model has been trained on a different number of tokens, with the 27B model being trained on 14 trillion tokens. This extensive training is expected to yield better performance, particularly for the 12B model, which is anticipated to be a strong contender for various tasks. The models have also undergone improvements in their architecture and data filtering techniques, contributing to better reasoning and mathematical capabilities.
The video showcases several practical applications of the Gemma 3 models, including their ability to analyze images, generate stories based on multiple images, and perform optical character recognition (OCR). The models can also handle text prompts effectively, allowing users to request specific outputs, such as detailed captions or translations. The 12B model, in particular, is highlighted for its strong performance, making it suitable for local deployment without the need for cloud-based solutions.
Finally, the video provides insights into how users can implement the Gemma 3 models using the Transformers library, emphasizing the ease of use through pipelines and conditional generation classes. The models are expected to be available on various platforms, including Google Cloud and Kaggle, making them accessible for a wide range of applications. The presenter encourages viewers to explore the capabilities of the Gemma 3 models for research and practical use, highlighting their potential in both local and cloud environments.