update readme

README.md

@@ -23,19 +23,14 @@ Our vision for KTransformers is to serve as a flexible platform for experimentin
 
 <h2 id="Updates">🔥 Updates</h2>
 
+* **July 26, 2025**: Support SmallThinker and GLM4-MoE. ([Tutorial](./doc/en/SmallThinker_and_Glm4moe.md))
 * **July 11, 2025**: Support Kimi-K2. ([Tutorial](./doc/en/Kimi-K2.md))
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 * **June 30, 2025**: Support 3-layer (GPU-CPU-Disk) [prefix cache](./doc/en/prefix_cache.md) reuse.
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 * **May 14, 2025**: Support Intel Arc GPU ([Tutorial](./doc/en/xpu.md)).
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 * **Apr 29, 2025**: Support AMX-Int8、 AMX-BF16 and Qwen3MoE ([Tutorial](./doc/en/AMX.md))
 
 https://github.com/user-attachments/assets/fafe8aec-4e22-49a8-8553-59fb5c6b00a2
 
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 * **Apr 9, 2025**: Experimental support for LLaMA 4 models ([Tutorial](./doc/en/llama4.md)).
 * **Apr 2, 2025**: Support Multi-concurrency. ([Tutorial](./doc/en/balance-serve.md)).
 
@@ -65,7 +60,7 @@ https://github.com/user-attachments/assets/ebd70bfa-b2c1-4abb-ae3b-296ed38aa285
 </p>
 
 - **[NEW!!!] Local 671B DeepSeek-Coder-V3/R1:** Running its Q4_K_M version using only 14GB VRAM and 382GB DRAM([Tutorial](./doc/en/DeepseekR1_V3_tutorial.md)).
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   - Prefill Speed (tokens/s):
     - KTransformers: 54.21 (32 cores) → 74.362 (dual-socket, 2×32 cores) → 255.26 (optimized AMX-based MoE kernel, V0.3 only) → 286.55 (selectively using 6 experts, V0.3 only)
     - Compared to 10.31 tokens/s in llama.cpp with 2×32 cores, achieving up to **27.79× speedup**.
@@ -131,7 +126,6 @@ we have already supported vendors:
 - Kunpeng
 - AMD
 
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 ### 📥 Installation
 
 To install KTransformers, follow the official [Installation Guide](https://kvcache-ai.github.io/ktransformers/en/install.html).
@@ -201,3 +195,4 @@ If you have any questions, feel free to open an issue. Alternatively, you can jo
 <h2 id="FAQ">🙋 FAQ</h2>
 
 Some common questions are answered in the [FAQ](doc/en/FAQ.md).
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