From 0ec33c9a76b7bb710ac98700b6fa23afae2f07e5 Mon Sep 17 00:00:00 2001 From: cuiunbo Date: Thu, 23 May 2024 14:33:02 +0800 Subject: [PATCH] Update Phi-3-vision-128k-instruct benchmark result --- README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 2273c0d..f5766a4 100644 --- a/README.md +++ b/README.md @@ -25,7 +25,7 @@ ## News -* [2024.05.24] We've released a comprehensive comparison between Phi-3-vision-128k-instruct and MiniCPM-Llama3-V 2.5, including benchmarks evaluations, and multilingual capabilities 🌟📊🌍. Click [here](#Evaluation) to view more details. +* [2024.05.24] We've released a comprehensive comparison between Phi-3-vision-128k-instruct and MiniCPM-Llama3-V 2.5, including benchmarks evaluations, and multilingual capabilities 🌟📊🌍. Click [here](#evaluation) to view more details. * [2024.05.20] We open-soure MiniCPM-Llama3-V 2.5, it has improved OCR capability and supports 30+ languages, representing the first edge-side MLLM achieving GPT-4V level performance! We provide [efficient inference](#deployment-on-mobile-phone) and [simple fine-tuning](./finetune/readme.md). Try it now! * [2024.04.23] MiniCPM-V-2.0 supports vLLM now! Click [here](#vllm) to view more details. * [2024.04.18] We create a HuggingFace Space to host the demo of MiniCPM-V 2.0 at [here](https://huggingface.co/spaces/openbmb/MiniCPM-V-2)! @@ -41,6 +41,7 @@ - [MiniCPM-Llama3-V 2.5](#minicpm-llama3-v-25) + - [Evaluation](#evaluation) - [MiniCPM-V 2.0](#minicpm-v-20) - [Online Demo](#online-demo) - [Install](#install) @@ -75,7 +76,7 @@ - 🚀 **Efficient Deployment.** MiniCPM-Llama3-V 2.5 systematically employs **model quantization, CPU optimizations, NPU optimizations and compilation optimizations**, achieving high-efficiency deployment on edge devices. For mobile phones with Qualcomm chips, we have integrated the NPU acceleration framework QNN into llama.cpp for the first time. After systematic optimization, MiniCPM-Llama3-V 2.5 has realized a **150-fold acceleration in multimodal large model edge-side image encoding** and a **3-fold increase in language decoding speed**. -### Evaluation +### Evaluation