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Meet Kimi-VL and Kimi-VL-Thinking
- Kimi-VL is an open-source vision-language model by Kimi.ai, using a Mixture-of-Experts framework with 3 billion parameters for computational efficiency and MoonViT for high-resolution visuals.
- It excels in long-context tasks with windows up to 128K tokens, achieving high scores on benchmarks like OCRBench, MMLongBench-Doc, and LongVideoBench.
- Kimi-VL-Thinking enhances Kimi-VL with advanced reasoning skills for mathematical and logical tasks, scoring well on MathVision and ScreenSpot-Pro benchmarks.
- Both models optimize resource usage with a MoE architecture, activating ~3B parameters for scalability and fast inference, making them ideal for real-world applications.
- MoonViT vision encoder enables native processing of high-res images for improved performance in tasks like OCR, surpassing competitors in text extraction.
- Trained on a diverse dataset including mathematics, coding, and knowledge domains, Kimi-VL-Thinking uses Chain-of-Thought reasoning to solve complex problems efficiently.
- Their MoE architecture reduces computational overhead while maintaining high performance, demonstrating efficiency in multimodal tasks.
- Kimi-VL-Thinking excels in mathematical reasoning and agent tasks like UI navigation, making it a versatile choice for applications requiring logical analysis.
- Kimi.ai releases these models under MIT licenses, fostering community collaboration and innovation in AI development.
- Kimi-VL and Kimi-VL-Thinking offer cost-effective solutions for enterprises seeking to process multimodal data efficiently, supported by accessible weights and documentation on Hugging Face.
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