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README.md

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 We are excited to announce the official open-source release of Ring-flash-linear-2.0!
 
-Building on the success of our Ling 2.0 series, this model continues to leverage a powerful hybrid architecture of linear and standard attention, perfectly balancing high performance with superior efficiency. By integrating our proven MoE design with optimizations like a 1/32 expert activation ratio and MTP layers, Ring-flash-linear achieves the performance of a 40 B dense model while activating only 6.1 B parameters.  This model was converted from [Ling-flash-base-2.0](https://huggingface.co/inclusionAI/Ling-flash-base-2.0), further trained on an additional 1T tokens.
+Building on the success of our Ling 2.0 series, this model continues to leverage a powerful hybrid architecture of linear and standard attention, perfectly balancing high performance with superior efficiency. By integrating our proven MoE design with optimizations like a 1/32 expert activation ratio and MTP layers, Ring-flash-linear achieves the performance of a 40B dense model while activating only 6.1B parameters.  This model was converted from [Ling-flash-base-2.0](https://huggingface.co/inclusionAI/Ling-flash-base-2.0), further trained on an additional 1T tokens.
 When it comes to benchmarks, Ring-flash-linear-2.0 not only holds its own against standard attention models (like Ring-flash-2.0) but also outperforms other open-source MoE and Dense models in its class on several demanding tasks. Plus, with support for a 128k long context, it's faster and more precise than ever, especially when handling long-form inputs and outputs.
 
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 ## Evaluation
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+To better demonstrate the model's capabilities, we selected representative open-source thinking models and closed-source APIs for comparison. 
+We present results on several challenging reasoning benchmarks spanning domains such as mathematics, coding, and science. Also, we evaluate the model's performance on a creative writing task (Creative Writing v3). 
+We observe that our model achieves performance on par with other models.
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 ## Linear Attention, Highly Sparse, High-Speed Generation
 
-Thanks to its hybrid attention mechanism and highly sparse MoE architecture, Ring-flash-linear-2.0 achieves near-linear time complexity and constant space complexity, resulting in outstanding inference efficiency. To fully demonstrate this advantage, we conducted a end-to-end comparison between our model and top-tier competitors of similar size or performance.
-What is truly exciting is that in the comparison with Qwen3-32B, Ring-flash-linear-2.0 demonstrates a remarkable advantage in inference efficiency. During the prefill phase, when the context length exceeds 32k, its throughput approaches 5 times that of the former. Its performance in the high-concurrency decoding phase is even more impressive, when generating a length of 32k, Ring-flash-linear-2.0 already boasts a significant throughput advantage of 4 times. When the generated length reaches 64k, this advantage surges to nearly 10 times!
+Thanks to its hybrid attention mechanism and highly sparse MoE architecture, Ring-flash-linear-2.0 achieves near-linear time complexity and constant space complexity, resulting in outstanding inference efficiency. 
+To fully demonstrate this advantage, we conducted a comparison between our model and top-tier competitors of similar size or performance.
+The results clearly demonstrate the advantage of our model in inference efficiency.
+
 
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