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Paligemma 2 Mix
Overview :
PaliGemma 2 mix is an upgraded vision language model from Google, belonging to the Gemma family. It can handle various vision and language tasks, such as image segmentation, video captioning, and scientific question answering. The model provides pre-trained checkpoints in different sizes (3B, 10B, and 28B parameters), making it easy to fine-tune for a variety of visual language tasks. Its main advantages are versatility, high performance, and developer-friendliness, supporting multiple frameworks (such as Hugging Face Transformers, Keras, PyTorch, etc.). This model is suitable for developers and researchers who need to efficiently process vision and language tasks, significantly improving development efficiency.
Target Users :
This product is ideal for developers, researchers, and professionals in related fields who need to process vision and language tasks. It helps them quickly implement complex visual language applications, improve development efficiency, and supports various frameworks and tools, lowering the barrier to entry.
Use Cases
Use PaliGemma 2 mix to generate accurate subtitles for short videos, improving content readability.
Help users quickly obtain key information from images through the visual question answering function.
In medical image analysis, use the segmentation function to assist doctors in diagnosis.
Features
Supports multiple tasks, such as generating short and long captions, OCR, visual question answering, object detection, and segmentation
Offers multiple model sizes (3B, 10B, 28B parameters) and resolutions (224px and 448px) to meet different needs
Compatible with various development frameworks, including Hugging Face Transformers, Keras, PyTorch, JAX, etc.
Can be upgraded directly from the original PaliGemma model without modification
Provides detailed official documentation and example code for developers to get started quickly
Supports direct deployment and fine-tuning in Vertex Model Garden
Allows quick experience of model capabilities through Hugging Face demos
Demonstrates excellent performance in various tasks, making it suitable for a wide range of applications
How to Use
1. Visit the Hugging Face demo page to quickly experience the capabilities of PaliGemma 2 mix.
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2. Download the model weights from Kaggle or Hugging Face to obtain local usage rights.
3. Use Keras inference notebooks to run the model in Google Colab or a local environment.
4. Deploy and fine-tune the model directly in Vertex Model Garden to adapt it to specific tasks or domains.
5. Refer to the official documentation to learn how to specify tasks using prompt syntax, such as 'caption en' for generating subtitles.
6. Use Hugging Face Transformers example code for fine-tuning and deployment, quickly integrating it into existing projects.
7. Refer to the official example notebooks to learn how to use PaliGemma 2 mix in different frameworks.
8. Choose the appropriate model size and resolution based on actual needs to optimize performance and resource consumption.
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