

Kimi VL
Overview :
Kimi-VL is an advanced expert-mixed visual language model designed for multi-modal reasoning, long-context understanding, and powerful agent capabilities. This model excels in several complex domains, boasting efficient 2.8B parameters while exhibiting outstanding mathematical reasoning and image understanding capabilities. Kimi-VL sets a new standard for multi-modal models with its optimized computational performance and ability to handle long inputs.
Target Users :
Kimi-VL is suitable for users who need complex reasoning and multi-modal interaction, especially researchers and developers. It significantly improves efficiency and accuracy when handling images, text, and their combinations.
Use Cases
In education, Kimi-VL can be used to help students solve mathematical problems and understand image content.
In business analysis, Kimi-VL can process and analyze long documents to extract key information.
In developer tools, Kimi-VL can be integrated into applications to enhance user interaction with visual content.
Features
Multi-modal Reasoning: Supports complex multi-turn interactions and reasoning tasks.
Long Context Processing: Features a 128K extended context window, accommodating long texts and diverse inputs.
Mathematical Reasoning Capabilities: Provides powerful mathematical solutions through specialized optimization.
Ultra-High-Resolution Visual Input Understanding: Processes high-resolution images and performs accurate understanding.
Efficient Computation: Delivers high-performance output while maintaining low computational costs.
OCR Support: Enables optical character recognition, suitable for text extraction tasks.
Video Understanding: Possesses multi-image understanding and video content parsing capabilities.
Multiple Application Scenarios: Applicable to various scenarios such as education, research, and business analysis.
How to Use
1. Install dependencies and ensure your environment has Python 3.10 and the corresponding libraries.
2. Download the Kimi-VL model from Hugging Face and initialize it using AutoModelForCausalLM.
3. Load the image to be processed and prepare the input message.
4. Use the processor to merge the image and text into the input format required by the model.
5. Run the model to generate output and process the returned results.
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