From 137d4454e059145034cc9d145708c97019089542 Mon Sep 17 00:00:00 2001 From: yiranyyu <2606375857@qq.com> Date: Tue, 28 May 2024 18:47:20 +0800 Subject: [PATCH] update readme --- README.md | 7 +++---- README_en.md | 7 +++---- README_zh.md | 6 ++---- 3 files changed, 8 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index 66cf7ef..e70ffa2 100644 --- a/README.md +++ b/README.md @@ -84,13 +84,12 @@ - 🌏 **Multilingual Support.** Thanks to the strong multilingual capabilities of Llama 3 and the cross-lingual generalization technique from [VisCPM](https://github.com/OpenBMB/VisCPM), MiniCPM-Llama3-V 2.5 extends its bilingual (Chinese-English) multimodal capabilities to **over 30 languages including German, French, Spanish, Italian, Korean etc.** [All Supported Languages](./assets/minicpm-llama-v-2-5_languages.md). -- 💫 **Easy Usage.** -MiniCPM-Llama3-V 2.5 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md) and [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5/examples/minicpm-v2.5) support for efficient CPU inference on local devices, (2) [GGUF](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) format quantized models in 16 sizes, (3) efficient [LoRA](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#lora-finetuning) fine-tuning with only 2 V100 GPUs, (4) [streaming output](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5#usage), (5) quick local WebUI demo setup in [Gradio](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_2.5.py) and [Streamlit](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_streamlit-2_5.py), and (6) interactive demos in [HuggingFace Spaces](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5). - - - 🚀 **Efficient Deployment.** MiniCPM-Llama3-V 2.5 systematically employs **model quantization, CPU optimizations, NPU optimizations and compilation optimizations**, achieving high-efficiency deployment on end-side 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 **150x acceleration in end-side MLLM image encoding** and a **3x speedup in language decoding**. +- 💫 **Easy Usage.** +MiniCPM-Llama3-V 2.5 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md) and [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5/examples/minicpm-v2.5) support for efficient CPU inference on local devices, (2) [GGUF](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) format quantized models in 16 sizes, (3) efficient [LoRA](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#lora-finetuning) fine-tuning with only 2 V100 GPUs, (4) [streaming output](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5#usage), (5) quick local WebUI demo setup in [Gradio](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_2.5.py) and [Streamlit](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_streamlit-2_5.py), and (6) interactive demos in [HuggingFace Spaces](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5). + ### Evaluation
diff --git a/README_en.md b/README_en.md index 66cf7ef..e70ffa2 100644 --- a/README_en.md +++ b/README_en.md @@ -84,13 +84,12 @@ - 🌏 **Multilingual Support.** Thanks to the strong multilingual capabilities of Llama 3 and the cross-lingual generalization technique from [VisCPM](https://github.com/OpenBMB/VisCPM), MiniCPM-Llama3-V 2.5 extends its bilingual (Chinese-English) multimodal capabilities to **over 30 languages including German, French, Spanish, Italian, Korean etc.** [All Supported Languages](./assets/minicpm-llama-v-2-5_languages.md). -- 💫 **Easy Usage.** -MiniCPM-Llama3-V 2.5 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md) and [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5/examples/minicpm-v2.5) support for efficient CPU inference on local devices, (2) [GGUF](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) format quantized models in 16 sizes, (3) efficient [LoRA](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#lora-finetuning) fine-tuning with only 2 V100 GPUs, (4) [streaming output](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5#usage), (5) quick local WebUI demo setup in [Gradio](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_2.5.py) and [Streamlit](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_streamlit-2_5.py), and (6) interactive demos in [HuggingFace Spaces](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5). - - - 🚀 **Efficient Deployment.** MiniCPM-Llama3-V 2.5 systematically employs **model quantization, CPU optimizations, NPU optimizations and compilation optimizations**, achieving high-efficiency deployment on end-side 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 **150x acceleration in end-side MLLM image encoding** and a **3x speedup in language decoding**. +- 💫 **Easy Usage.** +MiniCPM-Llama3-V 2.5 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md) and [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5/examples/minicpm-v2.5) support for efficient CPU inference on local devices, (2) [GGUF](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) format quantized models in 16 sizes, (3) efficient [LoRA](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#lora-finetuning) fine-tuning with only 2 V100 GPUs, (4) [streaming output](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5#usage), (5) quick local WebUI demo setup in [Gradio](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_2.5.py) and [Streamlit](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_streamlit-2_5.py), and (6) interactive demos in [HuggingFace Spaces](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5). + ### Evaluation
diff --git a/README_zh.md b/README_zh.md index c97b750..3b59758 100644 --- a/README_zh.md +++ b/README_zh.md @@ -85,13 +85,11 @@ - 🌏 **多语言支持。** 得益于 Llama 3 强大的多语言能力和 VisCPM 的跨语言泛化技术，MiniCPM-Llama3-V 2.5 在中英双语多模态能力的基础上，仅通过少量翻译的多模态数据的指令微调，高效泛化支持了**德语、法语、西班牙语、意大利语、韩语等 30+ 种语言**的多模态能力，并表现出了良好的多语言多模态对话性能。[查看所有支持语言](./assets/minicpm-llama-v-2-5_languages.md) -- 💫 **易于使用。** - MiniCPM-Llama3-V 2.5 可以通过多种方式轻松使用：（1）[llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md) 和 [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5/examples/minicpm-v2.5) 支持在本地设备上进行高效的 CPU 推理；（2）提供 16 种尺寸的 [GGUF](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) 格式量化模型；（3）仅需 2 张 V100 GPU 即可进行高效的 [LoRA](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#lora-finetuning) 微调；（ 4）支持[流式输出](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5#usage)；（5）快速搭建 [Gradio](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_2.5.py) 和 [Streamlit](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_streamlit-2_5.py) 本地 WebUI demo；（ 6.）[HuggingFace Spaces](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5) 交互式 demo。 - - 🚀 **高效部署。** MiniCPM-Llama3-V 2.5 较为系统地通过**模型量化、CPU、NPU、编译优化**等高效加速技术，实现高效的终端设备部署。对于高通芯片的移动手机，我们首次将 NPU 加速框架 QNN 整合进了 llama.cpp。经过系统优化后，MiniCPM-Llama3-V 2.5 实现了多模态大模型端侧**语言解码速度 3 倍加速**、**图像编码 150 倍加速**的巨大提升。 - +- 💫 **易于使用。** + MiniCPM-Llama3-V 2.5 可以通过多种方式轻松使用：（1）[llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md) 和 [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5/examples/minicpm-v2.5) 支持在本地设备上进行高效的 CPU 推理；（2）提供 16 种尺寸的 [GGUF](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) 格式量化模型；（3）仅需 2 张 V100 GPU 即可进行高效的 [LoRA](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#lora-finetuning) 微调；（ 4）支持[流式输出](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5#usage)；（5）快速搭建 [Gradio](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_2.5.py) 和 [Streamlit](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_streamlit-2_5.py) 本地 WebUI demo；（ 6.）[HuggingFace Spaces](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5) 交互式 demo。 ### 性能评估