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Ferret UI Llama8b
Ferret-UI-Llama8b
F
Ferret UI Llama8b
AI ModelDevelopment & Tools#Multimodal#Large Language Model#Image-to-Text#Dialogue System#Text GenerationStandard PicksOpen Source
Overview :
Ferret-UI is the first multimodal large language model (MLLM) centered on user interfaces, specifically designed for gesture expression, localization, and reasoning tasks. Built on Gemma-2B and Llama-3-8B, it is capable of performing complex user interface tasks. This version aligns with Apple's research paper and serves as a powerful tool for image-to-text tasks, excelling in dialogue and text generation.
Target Users :
The target audience includes developers and researchers, particularly those working in the field of artificial intelligence who need to handle image and text data and develop applications based on language models. This model can assist them in building smarter interfaces, enhancing user experience, and establishing deeper connections between images and text.
Total Visits： 29.7M
Top Region： US(17.94%)
Website Views ： 53.8K
Use Cases
Example 1: Use the Ferret-UI-Llama8b model to generate product descriptions for an e-commerce website.
Example 2: Utilize the model in a customer support system to understand user-uploaded screenshots and provide relevant assistance.
Example 3: Use image recognition and text description to aid students in learning complex concepts within educational software.
Features
? Gesture Expression: Capable of understanding and processing gesture expressions within images.
? Localization: Determines the position of specific objects within an image.
? Reasoning Tasks: Executes complex reasoning based on image and text information.
? Image-to-Text: Converts image content into text descriptions.
? Dialogue System: Supports dialogue interactions based on images and text.
? Text Generation: Generates relevant text based on image content.
? Multimodal Interaction: Combines image and text information for interaction.
? Custom Code Support: Allows users to customize model behavior as needed.
How to Use
1. Download the necessary Python files: builder.py, conversation.py, inference.py, model_UI.py, mm_utils.py.
2. Prepare image files and prompt text.
3. Call the inference_and_run function, passing in the image path and prompt text.
4. If needed, specify a bounding box to indicate specific areas within the image.
5. Execute the function and obtain the model-generated text output.
6. Analyze the output text and perform subsequent processing based on the application context.
7. If necessary, integrate templates from GROUNDING_TEMPLATES to enhance the model's localization and reasoning capabilities.
8. Customize the model as needed to fit specific business logic.
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