Update README.md

README.md

@@ -49,8 +49,8 @@ When it comes to benchmarks, Ring-flash-linear-2.0 not only holds its own agains
 
 ## 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 head-to-head 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! Even when compared to the newly emerging hybrid attention based model, Qwen3-Next-80BA3B, although Ring-flash-linear-2.0 has a larger model size, which puts it at a disadvantage in terms of IO, its higher proportion of linear attention layers and the more efficient implementation of linear attention still grant it superior inference efficiency over Qwen3-Next-80BA3B.
+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!
 
 <div style="display: flex; justify-content: center; align-items: flex-start; gap: 20px;">
   <div style="text-align: center;">