Kimi-VL-A3B-Thinking
| Property | Value |
|---|---|
| Total Parameters | 16B |
| Active Parameters | 2.8B |
| Context Length | 128K |
| Model Type | Vision-Language Model (VLM) |
| Architecture | MoE with MoonViT Vision Encoder |
| Hugging Face | moonshotai/Kimi-VL-A3B-Thinking |
What is Kimi-VL-A3B-Thinking?
Kimi-VL-A3B-Thinking is an advanced vision-language model that combines efficient parameter usage with powerful reasoning capabilities. It's built on a Mixture-of-Experts architecture that uses only 2.8B active parameters out of 16B total, making it highly efficient while maintaining strong performance. The model excels particularly in mathematical reasoning and long-chain thinking tasks, achieving impressive scores on benchmarks like MathVision (36.8) and MathVista (71.3).
Implementation Details
The model architecture consists of three main components: an MoE language model, a native-resolution visual encoder (MoonViT), and an MLP projector. It's been specifically enhanced through long chain-of-thought supervised fine-tuning and reinforcement learning to improve its reasoning capabilities.
- Native-resolution vision processing with MoonViT encoder
- 128K token context window for extended input processing
- Optimized for temperature setting of 0.6
- Supports multi-turn agent interaction tasks
Core Capabilities
- Advanced mathematical reasoning and problem-solving
- Long-context understanding and processing
- Multi-image and video comprehension
- Optical character recognition (OCR)
- College-level image and video analysis
- Ultra-high-resolution visual input processing
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to achieve high performance with only 2.8B active parameters while matching or exceeding the capabilities of much larger models (30B/70B) in specific tasks makes it unique. Its extended context window and native-resolution processing capabilities set it apart from other VLMs.
Q: What are the recommended use cases?
The model is particularly well-suited for advanced mathematical reasoning tasks, long-form content analysis, complex visual understanding, and multi-turn interactions requiring detailed thinking and analysis. It's optimized for scenarios requiring step-by-step reasoning and detailed problem-solving.
