Update README.md

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-mini-linear achieves the performance of a 8 B dense model while activating only 1.4 B parameters.  This model was converted from [Ling-mini-base-2.0](https://huggingface.co/inclusionAI/Ling-mini-base-2.0-20T), further trained on an additional 600 B tokens.
-When it comes to benchmarks, Ring-mini-linear-2.0 not only holds its own against standard attention models (like ring-mini-2) but also outperforms other open-source MoE and Dense models in its class on several demanding tasks. Plus, with native support for a 128k long context, it's faster and more precise than ever, especially when handling long-form inputs and outputs. -->
+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 1 T tokens.
+When it comes to benchmarks, Ring-flash-linear-2.0 not only holds its own against standard attention models (like ring-flash-2) 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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@@ -51,33 +51,33 @@ Here is a demo of a small Snake game, with the code generated by our model.
 
 ## Linear Attention, Highly Sparse，High-Speed Generation
 
-<!-- Thanks to its hybrid attention mechanism and highly sparse MoE architecture, Ring-mini-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.
-The results are remarkable. In the prefill stage, Ring-mini-linear-2.0's performance is exceptional; when the context length exceeds 256k, its throughput is over 12 times higher than that of Qwen3-8B. Furthermore, in the high-concurrency decode stage, its capabilities are even more pronounced. For generation lengths exceeding 32k, its throughput easily surpasses 12 times that of Qwen3-8B.
+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.
 
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-    <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/O9gHLIOCdpWvBbPC6bMM5.webp" width="500">
-    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 4:</strong> Ring-mini-linear-2.0 prefill throughput</p>
+    <img src="https://mdn.alipayobjects.com/huamei_t783ie/afts/img/wtM_TJ4KVqYAAAAARpAAAAgADgCDAQFr/original" width="500">
+    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 4:</strong> Ring-flash-linear-2.0 prefill throughput</p>
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     <p align="center">
-      <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/AvMTStWFX-Frzv-vOzyr6.webp" width="500">
+      <img src="https://mdn.alipayobjects.com/huamei_t783ie/afts/img/3n9lSZscvBwAAAAAUhAAAAgADgCDAQFr/original" width="500">
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-    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 5:</strong> Ring-mini-linear-2.0 decode throughput</p>
+    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 5:</strong> Ring-flash-linear-2.0 decode throughput</p>
   </div>
 
-</div> -->
+</div>
 
 
 ## Model Downloads
 
-<!-- <div align="center">
+<div align="center">
 
 |     **Model**      | **#Total Params** | **#Activated Params** | **Context Length** | **Download** |
 | :----------------: | :---------------: | :-------------------: | :----------------: | :----------: |
-| Ring-mini-linear-2.0 |       16.8B       |         1.4B         |        128K         |      [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ring-mini-linear-2.0) <br>[🤖 Modelscope](https://modelscope.cn/models/inclusionAI/Ring-mini-linear-2.0)|
-</div> -->
+| Ring-flash-linear-2.0 |       100B       |         6.1B         |        128K         |      [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ring-flash-linear-2.0) <br>[🤖 Modelscope](https://modelscope.cn/models/inclusionAI/Ring-flash-linear-2.0)|
+</div>
 
 ## Quickstart