paligemma-3b-pt-896
Maintainer: google
53
🧪
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
The paligemma-3b-pt-896 is a versatile and lightweight vision-language model (VLM) from Google. It is inspired by PaLI-3 and based on open components such as the SigLIP vision model and the Gemma language model. Similar to the paligemma-3b-pt-224 and paligemma-3b-pt-448 models, it takes both image and text as input and generates text as output, supporting multiple languages.
Model inputs and outputs
Inputs
- Image: An image to be captioned or a question to be answered about an image.
- Text: A prompt to caption the image, or a question about the image.
Outputs
- Text: A caption describing the image, an answer to a question about the image, object bounding box coordinates, or segmentation codewords.
Capabilities
The paligemma-3b-pt-896 model is designed for class-leading fine-tuning performance on a wide range of vision-language tasks such as image and short video caption, visual question answering, text reading, object detection and object segmentation. It can handle tasks in multiple languages thanks to its training on the WebLI dataset.
What can I use it for?
The paligemma-3b-pt-896 model can be useful for a variety of applications that involve combining vision and language, such as:
- Generating captions for images or short videos
- Answering questions about images
- Detecting and localizing objects in images
- Segmenting images into semantic regions
To use the model, you can fine-tune it on your specific task and dataset using the techniques described in the Responsible Generative AI Toolkit.
Things to try
One interesting aspect of the paligemma-3b-pt-896 model is its ability to handle tasks in multiple languages. You could experiment with providing prompts in different languages and observe the model's performance on translation, multilingual question answering, or cross-lingual image captioning. Additionally, you could explore the model's few-shot or zero-shot capabilities by fine-tuning it on a small dataset and evaluating its performance on related tasks.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
💬
paligemma-3b-pt-224
58
The paligemma-3b-pt-224 model is a versatile and lightweight vision-language model (VLM) from Google. It is inspired by the PaLI-3 model and based on open components like the SigLIP vision model and the Gemma language model. The paligemma-3b-pt-224 takes both image and text as input and generates text as output, supporting multiple languages. It is designed for fine-tune performance on a wide range of vision-language tasks such as image and short video captioning, visual question answering, text reading, object detection and object segmentation. Model inputs and outputs Inputs Image and text string**: The model takes an image and a text prompt as input, such as a question to answer about the image or a request to caption the image. Outputs Generated text**: The model outputs generated text in response to the input, such as a caption of the image, an answer to a question, a list of object bounding box coordinates, or segmentation codewords. Capabilities The paligemma-3b-pt-224 model is a versatile vision-language model capable of a variety of tasks. It can generate captions for images, answer questions about visual content, detect and localize objects in images, and even produce segmentation maps. Its broad capabilities make it useful for applications like visual search, content moderation, and intelligent assistants. What can I use it for? The paligemma-3b-pt-224 model can be used in a wide range of applications that involve both text and visual data. For example, it could power an image captioning tool to automatically describe the contents of photos, or a visual question answering system that can answer queries about images. It could also be used to build smart assistants that can understand and respond to multimodal inputs. The model's open-source nature makes it accessible for developers to experiment and integrate into their own projects. Things to try One interesting thing to try with the paligemma-3b-pt-224 model is fine-tuning it on a specific domain or task. The maintainers provide fine-tuning scripts and notebooks for the Gemma model family that could be adapted for the paligemma-3b-pt-224. This allows you to further specialize the model's capabilities for your particular use case, unlocking new potential applications.
Updated Invalid Date
🤯
paligemma-3b-mix-448
51
The paligemma-3b-mix-448 model is a versatile and lightweight vision-language model (VLM) from Google. It is inspired by PaLI-3 and based on open components such as the SigLIP vision model and the Gemma language model. Compared to the pre-trained paligemma-3b-pt-224 model, this "mix" model has been fine-tuned on a mixture of downstream academic tasks, with the input increased to 448x448 images and 512 token text sequences. This allows it to perform better on a wide range of vision-language tasks. Similar models in the PaliGemma family include the pre-trained paligemma-3b-pt-224 and paligemma-3b-pt-896 versions, which have different input resolutions but are not fine-tuned on downstream tasks. Model inputs and outputs Inputs Image**: An image, such as a photograph or diagram. Text**: A text prompt, such as a caption for the image or a question about the image. Outputs Text**: Generated text in response to the input, such as a caption of the image, an answer to a question, a list of object bounding box coordinates, or segmentation codewords. Capabilities The paligemma-3b-mix-448 model is capable of a wide range of vision-language tasks, including image captioning, visual question answering, text reading, object detection, and object segmentation. It can handle multiple languages and has been designed for class-leading fine-tuning performance on these types of tasks. What can I use it for? You can fine-tune the paligemma-3b-mix-448 model on specific vision-language tasks to create custom applications. For example, you could fine-tune it on a domain-specific image captioning task to generate captions for technical diagrams, or on a visual question answering task to build an interactive educational tool. The pre-trained model and fine-tuned versions can also serve as a foundation for researchers to experiment with VLM techniques, develop algorithms, and contribute to the advancement of the field. Things to try One interesting aspect of the paligemma-3b-mix-448 model is its ability to handle a variety of input resolutions. By leveraging the 448x448 input size, you can potentially achieve better performance on tasks that benefit from higher-resolution images, such as object detection and segmentation. Try experimenting with different input resolutions to see how it affects the model's outputs. Additionally, since this model has been fine-tuned on a mixture of downstream tasks, you can explore using different prompting strategies to get the model to focus on specific capabilities. For example, you could try prefixing your prompts with "detect" or "segment" to instruct the model to perform object detection or segmentation, respectively.
Updated Invalid Date
🛸
paligemma-3b-mix-224
52
paligemma-3b-mix-224 is a versatile and lightweight vision-language model (VLM) from Google inspired by PaLI-3. It is based on open components like the SigLIP vision model and the Gemma language model. The model takes both image and text as input, and generates text as output, supporting multiple languages. It is designed for strong performance on a wide range of vision-language tasks like image captioning, visual question answering, and object detection. Similar models in the PaliGemma family include the paligemma-3b-mix-448 which uses larger 448x448 input images, and the paligemma-3b-pt-224 and paligemma-3b-pt-896 which are pre-trained models without fine-tuning. Model inputs and outputs Inputs Image:** An image to caption, answer a question about, or perform other vision-language tasks on. Text:** A prompt or question to condition the model's text generation, such as "caption this image in Spanish" or "what object is in this image?". Outputs Text:** The model's generated response to the input, such as a caption, answer to a question, or description of an object. Capabilities paligemma-3b-mix-224 excels at a variety of vision-language tasks. It can generate detailed and relevant captions for images, answer questions about image content, and even locate and describe specific objects. The model was fine-tuned on a wide range of academic datasets, allowing it to tackle everything from image captioning on COCO to visual question answering on VQAv2. What can I use it for? The paligemma-3b-mix-224 model can be used for a wide variety of applications that combine vision and language understanding. For example, you could use it to build an image captioning service for an e-commerce website, or a visual question answering system to help people who are blind or low-vision. The pre-trained model can also be fine-tuned on domain-specific datasets to tackle tasks like medical image analysis or scientific figure understanding. Things to try One interesting aspect of paligemma-3b-mix-224 is its ability to handle multiple languages. You can try prompting the model with text in different languages, like "caption this image en español" or "que objeto se ve en esta imagen?" to see how it performs on multilingual tasks. The model's strong performance on benchmarks like COCO-35L and XM3600 suggest it can generate high-quality multilingual captions. Another avenue to explore is using the model's object detection and segmentation capabilities. By conditioning the model with prompts like "detect the objects in this image" or "segment the cars in this image", you can get the model to output bounding boxes or segmentation masks in addition to text descriptions.
Updated Invalid Date
🤔
gemma-1.1-2b-it
93
The gemma-1.1-2b-it is an instruction-tuned version of the Gemma 2B language model from Google. It is part of the Gemma family of lightweight, state-of-the-art open models built using the same research and technology as Google's Gemini models. Gemma models are text-to-text, decoder-only large language models available in English, with open weights, pre-trained variants, and instruction-tuned variants. The 2B and 7B variants of the Gemma models offer different size and performance trade-offs, with the 2B model being more efficient and the 7B model providing higher performance. Model inputs and outputs Inputs Text string**: The model can take a variety of text inputs, such as a question, a prompt, or a document to be summarized. Outputs Generated English-language text**: The model produces text in response to the input, such as an answer to a question or a summary of a document. Capabilities The gemma-1.1-2b-it model is capable of a wide range of text generation tasks, including question answering, summarization, and reasoning. It can be used to generate creative text formats like poems, scripts, code, marketing copy, and email drafts. The model can also power conversational interfaces for customer service, virtual assistants, or interactive applications. What can I use it for? The Gemma family of models is well-suited for a variety of natural language processing and generation tasks. The instruction-tuned variants like gemma-1.1-2b-it can be particularly useful for applications that require following specific instructions or engaging in multi-turn conversations. Some potential use cases include: Content Creation**: Generate text for marketing materials, scripts, emails, or creative writing. Chatbots and Conversational AI**: Power conversational interfaces for customer service, virtual assistants, or interactive applications. Text Summarization**: Produce concise summaries of large text corpora, research papers, or reports. Research and Education**: Serve as a foundation for NLP research, language learning tools, or knowledge exploration. Things to try One key capability of the gemma-1.1-2b-it model is its ability to engage in coherent, multi-turn conversations. By using the provided chat template, you can prompt the model to maintain context and respond appropriately to a series of user inputs, rather than generating isolated responses. This makes the model well-suited for conversational applications, where maintaining context and following instructions is important. Another interesting aspect of the Gemma models is their relatively small size compared to other large language models. This makes them more accessible to deploy in resource-constrained environments like laptops or personal cloud infrastructure, democratizing access to state-of-the-art AI technology.
Updated Invalid Date