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README.md

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-  <a href="https://github.com/MoonshotAI/Kimi-Linear/blob/master/tech_report.pdf"><img src="figures/logo.png" height="16" width="16" style="vertical-align:middle"><b> Tech Report</b></a>  |  
-  <a href="https://huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Instruct"><img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" height="16" width="16" style="vertical-align:middle"><b> HuggingFace</b></a>
+  <a href="https://github.com/MoonshotAI/Kimi-Linear/blob/master/tech_report.pdf" ><img src="figures/logo.png" height="16" width="16" style="display: inline-block; vertical-align: middle; margin: 2px;"><b style="display: inline-block;"> Tech Report</b></a>  |  
+  <a href="https://huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Instruct"><img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" height="16" width="16" style="display: inline-block; vertical-align: middle; margin: 2px;"><b style="display: inline-block;"> HuggingFace</b></a>
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   <img width="90%" src="figures/perf_speed.png">
   <p><em><b>(a)</b> On MMLU-Pro (4k context length), Kimi Linear achieves 51.0 performance with similar speed as full attention. On RULER (128k context length), it shows Pareto-optimal performance (84.3) and 3.98x speedup. <b>(b)</b> Kimi Linear achieves 6.3x faster TPOT compared to MLA, offering significant speedups at long sequence lengths (1M tokens).</em></p>