Update README.md

README.md

@@ -21,7 +21,7 @@ tags:
 
 We are excited to announce the official open-source release of Ring-mini-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, further trained on an additional 600 B 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-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.
 
 <div style="display: flex; justify-content: center;">
@@ -32,18 +32,19 @@ When it comes to benchmarks, Ring-mini-linear-2.0 not only holds its own against
 </div>
 
 ## Evaluation
-<!-- <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/_tjjgBEBlankfrWUY0N9i.png" width="1000"> -->
+To properly evaluate the model's reasoning capabilities, we compared it against 3 other models—Ring-mini-2.0, Qwen3-8B-thinking, and GPT-OSS-20B-Medium—on 6 challenging reasoning benchmarks spanning mathematics, coding, and science. The results demonstrate that the performance of the hybrid linear architecture is by no means inferior to that of standard softmax attention; in fact, it even outperforms the other models on 3 of the benchmarks.
 <div style="display: flex; justify-content: center;">
   <div style="text-align: center;">
     <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/_tjjgBEBlankfrWUY0N9i.png" width="800">
-    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 3:</strong> demo </p>
+    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 2:</strong> Model Performance Comparison </p>
   </div>
 </div>
 
+Here is a demo of a small Snake game, with the code generated by our model.
 <div style="display: flex; justify-content: center;">
   <div style="text-align: center;">
-    <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/OyvwJdc0qRIJiHM1VJgwO.gif" width="800">
-    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 3:</strong> demo </p>
+    <img src="https://mdn.alipayobjects.com/huamei_jcuiuk/afts/img/tqfCQoTqRdAAAAAAgZAAAAgADr6CAQFr/original" width="800">
+    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 3:</strong> Snake Game </p>
   </div>
 </div>
 
@@ -81,8 +82,11 @@ The results are remarkable. In the prefill stage, Ring-mini-linear-2.0's perform
 ## Quickstart
 
 ### Requirements
-1. pip install flash-linear-attention==0.3.2
-2. pip install transformers==4.56.1
+
+```bash
+pip install flash-linear-attention==0.3.2
+pip install transformers==4.56.1
+```
 
 ### 🤗 Hugging Face Transformers
 
@@ -147,4 +151,4 @@ python -m sglang.launch_server \
 ```
 ### vLLM
  
-## Citation
+## Citation