Ring-mini-2.0: A Compact Powerhouse in AI Reasoning

The AI community has welcomed the launch of Ring-mini-2.0, a high-performance Mixture of Experts (MoE) model optimized from the Ling-mini-2.0 architecture. With a total parameter count of 16 billion, this model activates just 1.4 billion parameters during operation, delivering inference capabilities comparable to dense models below 10 billion parameters.

Performance and Capabilities

Ring-mini-2.0 shines in logical reasoning, programming, and mathematical tasks. It supports an impressive 128K context length, making it versatile across various applications. The model boasts a generation speed exceeding 300 tokens per second (token/s), with optimizations pushing this beyond 500 token/s.

Image source note: The image is AI-generated, and the image licensing service provider is Midjourney.

Enhanced Inference and Training

The model builds on the Ling-mini-2.0-base framework, undergoing deeper training to improve stability and generalization in complex reasoning tasks. Techniques like Long-COT SFT, large-scale RLVR, and RLHF were employed for joint optimization. Benchmark tests reveal that Ring-mini-2.0 outperforms dense models under 10B parameters and competes with larger MoE models, particularly in logical reasoning.

Efficiency by Design

Ring-mini-2.0 emphasizes efficiency through a 1/32 expert activation ratio and MTP layer architecture optimization, achieving performance akin to dense models of about 7–8 billion parameters. Its high sparsity and small activation design enable speeds over 300 token/s in H20 environments. Further cost reductions are achieved via Expert Dual Streaming optimization.

Open-Source Initiative

The team behind Ring-mini-2.0 has committed to full open-sourcing of the model weights, training strategies, and data recipes to foster academic and industrial research. This "small but excellent" model aims to become a preferred choice for compact inference models.

The future promises even larger, faster models under the Ling2.0 architecture.

Key Points:

• 16B parameter MoE model activating only 1.4B for inference.
• Excels in logical reasoning with a supported context length of 128K.
• Achieves speeds over 300 token/s (up to 500 token/s optimized).
• Outperforms dense sub-10B models in benchmarks.
• Fully open-sourced for research and application development.