update readme

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yiranyyu
2024-05-23 18:39:30 +08:00
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@@ -42,6 +42,12 @@ In most benchmarks, MiniCPM-Llama3-V 2.5 achieves **better performance** compare
1: L(ow): 448pxl, M(edium): 896pxl, H(igh): 1344pxl input images.
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1. Evaluation environment: A800 GPU, flash-attn=2.4.3, batch-size=1.
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MiniCPM-Llama3-V 2.5 shows better first token latency and throughput performance even though the number of parameters is twice as large as that of Phi-3-vision-128k-instruct due to its efficient image encoding method and adaptive resolution encoding strategy. For example, for an input images with a 448x448 resolution, MiniCPM-Llama3-V 2.5 encodes it into 96 tokens, while Phi-3-vision-128k-instruct encodes it into 2500+ tokens. Longer image token sequence significantly affects the first token latency and throughput. MiniCPM-V series models insist on obtaining stronger performance with more efficient encoding, thus achieves efficient end-device deployment and providing better experience for end users.
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得益于 MiniCPM-Llama3-V 2.5 高效的图像编码方式和自适应分辨率编码策略,即使参数量比 Phi-3-vision-128k-instruct 大一倍,依然展现出了更出色的首 token 延迟和吞吐量表现。例如两个模型对输入分辨率为448x448 的图像MiniCPM-Llama3-V 2.5 的图像编码长度为 96 而Phi-3-vision-128k-instruct 的图像编码长度为 2500+。更长的图像编码长度会显著影响首token延迟和吞吐量MiniCPM-V系列坚持用更高效的编码方式撬动更强的性能进而实现高效的终端设备部署为端侧用户提供更良好的体验。
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