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README.md

@@ -13,20 +13,7 @@ A Pocket-Sized MLLM for Ultra-Efficient Image and Video Understanding on Your Ph
 
 ## MiniCPM-V 4.6 Thinking
 
-**MiniCPM-V 4.6 Thinking** is the long chain-of-thought reasoning variant of [MiniCPM-V 4.6](https://huggingface.co/openbmb/MiniCPM-V-4.6). It generates an explicit reasoning trace before producing the final answer, substantially boosting performance on complex multimodal reasoning, math, and OCR-heavy tasks, while keeping the same edge-friendly architecture (SigLIP2-400M vision encoder + Qwen3.5-0.8B LLM) and the mixed 4x/16x visual token compression of MiniCPM-V 4.6. Notable features of MiniCPM-V 4.6 Thinking include:
-
-- 🔥 **Leading Foundation Capability.**
-  MiniCPM-V 4.6 scores 13 on the Artificial Analysis Intelligence Index benchmark, outperforming Qwen3.5-0.8B's score of 10 with 19x fewer token cost, and Qwen3.5-0.8B-Thinking's score of 11 with 43x fewer token cost. It also surpasses the larger Ministral 3 3B (score of 11).
-
-- 💪 **Strong Multimodal Capability.**
-  MiniCPM-V 4.6 outperforms Qwen3.5-0.8B on most vision-language understanding tasks, and reaches Qwen3.5 2B-level capability on many benchmarks including OpenCompass, RefCOCO, HallusionBench, MUIRBench, and OCRBench.
-- 🚀 **Ultra-Efficient Architecture.**
-  Based on the latest technique in [LLaVA-UHD v4](https://github.com/THUMAI-Lab/LLaVA-UHD-v4), MiniCPM-V 4.6 reduces the visual encoding computation FLOPs by more than 50%. It enables MiniCPM-V 4.6 to achieve better efficiency to even smaller models, achieving ~1.5x token throughput compared to Qwen3.5-0.8B. 
-  It also supports mixed 4x/16x visual token compression rate, allowing flexible switching between accuracy and speed.
-- 📱 **Broad Mobile Platform Coverage.**
-  MiniCPM-V 4.6 can be deployed across all three mainstream mobile platforms — iOS, Android, and HarmonyOS. With every edge adaptation code open-sourced, developers can reproduce the on-device experience in [just a few steps](#deploy-minicpm-v-46-on-ios-android-and-harmonyos-platforms).
-- 🛠️ **Developer Friendly.**
-  MiniCPM-V 4.6 is adapted to [inference frameworks](#use-minicpm-v-46-in-other-inference-and-training-frameworks) such as vLLM, SGLang, llama.cpp, Ollama, and supports [fine-tuning ecosystems](#use-minicpm-v-46-in-other-inference-and-training-frameworks) such as SWIFT and LLaMA-Factory. Developers can quickly customize models for new domains and tasks on consumer-grade GPUs. We provide multiple quantized variants across GGUF, BNB, AWQ, and GPTQ formats.
+**MiniCPM-V 4.6 Thinking** is the long chain-of-thought reasoning variant of [MiniCPM-V 4.6](https://huggingface.co/openbmb/MiniCPM-V-4.6). It generates an explicit reasoning trace before producing the final answer, substantially boosting performance on complex multimodal reasoning, math, and OCR-heavy tasks, while keeping the same edge-friendly architecture (SigLIP2-400M vision encoder + Qwen3.5-0.8B LLM) and the mixed 4x/16x visual token compression of MiniCPM-V 4.6.
 
 
 ### Evaluation <!-- omit in toc -->