Zamba2-mini
Advanced small language model designed for on-device applications.
Product Details
Zamba2-mini is a small language model released by Zyphra Technologies Inc., designed for device-side applications. It achieves evaluation scores and performance comparable to larger models while maintaining a very small memory footprint (<700MB). This model uses 4-bit quantization technology, which has the characteristics of 7 times parameter reduction while maintaining the same performance. Zamba2-mini excels in inference efficiency, with faster first token generation time, lower memory overhead, and lower generation latency than larger models such as Phi3-3.8B. Additionally, the model's weights have been released as open source (Apache 2.0), allowing researchers, developers, and companies to leverage its capabilities and push the boundaries of efficient base models.
Main Features
How to Use
Target Users
The target audience of Zamba2-mini is researchers, developers and companies seeking to deploy advanced AI systems on the device side. It is suitable for environments that have limited memory capacity and high requirements for inference speed, such as mobile devices, embedded systems, etc.
Examples
Language understanding and generation tasks in mobile applications.
Natural language interaction in embedded systems.
Fast text analysis and response on smart devices.
Quick Access
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