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update readme demo url

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Hongji Zhu
2024-10-16 16:36:50 +08:00
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README.md
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@@ -11,7 +11,7 @@ Join our <a href="docs/wechat.md" target="_blank"> 💬 WeChat</a> | View MiniC
<p align="center"> <p align="center">
MiniCPM-V 2.6 <a href="https://huggingface.co/openbmb/MiniCPM-V-2_6">🤗</a> <a href="https://huggingface.co/spaces/openbmb/MiniCPM-V-2_6">🤖</a> | MiniCPM-Llama3-V 2.5 <a href="https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5/">🤗</a> <a href="https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5">🤖</a> | MiniCPM-V 2.6 <a href="https://huggingface.co/openbmb/MiniCPM-V-2_6">🤗</a> <a href="http://120.92.209.146:8887/">🤖</a> | MiniCPM-Llama3-V 2.5 <a href="https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5/">🤗</a> <a href="https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5">🤖</a> |
<a href=https://arxiv.org/abs/2408.01800>MiniCPM-Llama3-V 2.5 Technical Report</a> <a href=https://arxiv.org/abs/2408.01800>MiniCPM-Llama3-V 2.5 Technical Report</a>
</p> </p>
@@ -105,7 +105,7 @@ Join our <a href="docs/wechat.md" target="_blank"> 💬 WeChat</a> | View MiniC
In addition to its friendly size, MiniCPM-V 2.6 also shows **state-of-the-art token density** (i.e., number of pixels encoded into each visual token). **It produces only 640 tokens when processing a 1.8M pixel image, which is 75% fewer than most models**. This directly improves the inference speed, first-token latency, memory usage, and power consumption. As a result, MiniCPM-V 2.6 can efficiently support **real-time video understanding** on end-side devices such as iPad. In addition to its friendly size, MiniCPM-V 2.6 also shows **state-of-the-art token density** (i.e., number of pixels encoded into each visual token). **It produces only 640 tokens when processing a 1.8M pixel image, which is 75% fewer than most models**. This directly improves the inference speed, first-token latency, memory usage, and power consumption. As a result, MiniCPM-V 2.6 can efficiently support **real-time video understanding** on end-side devices such as iPad.
- 💫 **Easy Usage.** - 💫 **Easy Usage.**
MiniCPM-V 2.6 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpmv-main/examples/llava/README-minicpmv2.6.md) and [ollama](https://github.com/OpenBMB/ollama/blob/minicpm-v2.6/examples/minicpm-v2.6/README.md) support for efficient CPU inference on local devices, (2) [int4](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4) and [GGUF](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) format quantized models in 16 sizes, (3) [vLLM](#inference-with-vllm) support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks, (5) quick local WebUI demo setup with [Gradio](#chat-with-our-demo-on-gradio), and (6) online web [demo](https://huggingface.co/spaces/openbmb/MiniCPM-V-2_6). MiniCPM-V 2.6 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpmv-main/examples/llava/README-minicpmv2.6.md) and [ollama](https://github.com/OpenBMB/ollama/blob/minicpm-v2.6/examples/minicpm-v2.6/README.md) support for efficient CPU inference on local devices, (2) [int4](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4) and [GGUF](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) format quantized models in 16 sizes, (3) [vLLM](#inference-with-vllm) support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks, (5) quick local WebUI demo setup with [Gradio](#chat-with-our-demo-on-gradio), and (6) online web [demo](http://120.92.209.146:8887/).
### Evaluation <!-- omit in toc --> ### Evaluation <!-- omit in toc -->
<div align="center"> <div align="center">
@@ -1238,7 +1238,7 @@ We provide online and local demos powered by Hugging Face Gradio <a href='https:
### Online Demo <!-- omit in toc --> ### Online Demo <!-- omit in toc -->
Click here to try out the online demo of [MiniCPM-V 2.6](https://huggingface.co/spaces/openbmb/MiniCPM-V-2_6) | [MiniCPM-Llama3-V 2.5](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5) | [MiniCPM-V 2.0](https://huggingface.co/spaces/openbmb/MiniCPM-V-2). Click here to try out the online demo of [MiniCPM-V 2.6](http://120.92.209.146:8887/) | [MiniCPM-Llama3-V 2.5](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5) | [MiniCPM-V 2.0](https://huggingface.co/spaces/openbmb/MiniCPM-V-2).
### Local WebUI Demo <!-- omit in toc --> ### Local WebUI Demo <!-- omit in toc -->

6
README_en.md
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@@ -11,7 +11,7 @@ Join our <a href="docs/wechat.md" target="_blank"> 💬 WeChat</a> | View MiniC
<p align="center"> <p align="center">
MiniCPM-V 2.6 <a href="https://huggingface.co/openbmb/MiniCPM-V-2_6">🤗</a> <a href="https://huggingface.co/spaces/openbmb/MiniCPM-V-2_6">🤖</a> | MiniCPM-Llama3-V 2.5 <a href="https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5/">🤗</a> <a href="https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5">🤖</a> | MiniCPM-V 2.6 <a href="https://huggingface.co/openbmb/MiniCPM-V-2_6">🤗</a> <a href="http://120.92.209.146:8887/">🤖</a> | MiniCPM-Llama3-V 2.5 <a href="https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5/">🤗</a> <a href="https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5">🤖</a> |
<a href=https://arxiv.org/abs/2408.01800>MiniCPM-Llama3-V 2.5 Technical Report</a> <a href=https://arxiv.org/abs/2408.01800>MiniCPM-Llama3-V 2.5 Technical Report</a>
</p> </p>
@@ -105,7 +105,7 @@ Join our <a href="docs/wechat.md" target="_blank"> 💬 WeChat</a> | View MiniC
In addition to its friendly size, MiniCPM-V 2.6 also shows **state-of-the-art token density** (i.e., number of pixels encoded into each visual token). **It produces only 640 tokens when processing a 1.8M pixel image, which is 75% fewer than most models**. This directly improves the inference speed, first-token latency, memory usage, and power consumption. As a result, MiniCPM-V 2.6 can efficiently support **real-time video understanding** on end-side devices such as iPad. In addition to its friendly size, MiniCPM-V 2.6 also shows **state-of-the-art token density** (i.e., number of pixels encoded into each visual token). **It produces only 640 tokens when processing a 1.8M pixel image, which is 75% fewer than most models**. This directly improves the inference speed, first-token latency, memory usage, and power consumption. As a result, MiniCPM-V 2.6 can efficiently support **real-time video understanding** on end-side devices such as iPad.
- 💫 **Easy Usage.** - 💫 **Easy Usage.**
MiniCPM-V 2.6 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpmv-main/examples/llava/README-minicpmv2.6.md) and [ollama](https://github.com/OpenBMB/ollama/blob/minicpm-v2.6/examples/minicpm-v2.6/README.md) support for efficient CPU inference on local devices, (2) [int4](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4) and [GGUF](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) format quantized models in 16 sizes, (3) [vLLM](#inference-with-vllm) support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks, (5) quick local WebUI demo setup with [Gradio](#chat-with-our-demo-on-gradio), and (6) online web [demo](https://huggingface.co/spaces/openbmb/MiniCPM-V-2_6). MiniCPM-V 2.6 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpmv-main/examples/llava/README-minicpmv2.6.md) and [ollama](https://github.com/OpenBMB/ollama/blob/minicpm-v2.6/examples/minicpm-v2.6/README.md) support for efficient CPU inference on local devices, (2) [int4](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4) and [GGUF](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) format quantized models in 16 sizes, (3) [vLLM](#inference-with-vllm) support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks, (5) quick local WebUI demo setup with [Gradio](#chat-with-our-demo-on-gradio), and (6) online web [demo](http://120.92.209.146:8887/).
### Evaluation <!-- omit in toc --> ### Evaluation <!-- omit in toc -->
<div align="center"> <div align="center">
@@ -1238,7 +1238,7 @@ We provide online and local demos powered by Hugging Face Gradio <a href='https:
### Online Demo <!-- omit in toc --> ### Online Demo <!-- omit in toc -->
Click here to try out the online demo of [MiniCPM-V 2.6](https://huggingface.co/spaces/openbmb/MiniCPM-V-2_6) | [MiniCPM-Llama3-V 2.5](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5) | [MiniCPM-V 2.0](https://huggingface.co/spaces/openbmb/MiniCPM-V-2). Click here to try out the online demo of [MiniCPM-V 2.6](http://120.92.209.146:8887/) | [MiniCPM-Llama3-V 2.5](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5) | [MiniCPM-V 2.0](https://huggingface.co/spaces/openbmb/MiniCPM-V-2).
### Local WebUI Demo <!-- omit in toc --> ### Local WebUI Demo <!-- omit in toc -->

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README_zh.md
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@@ -14,7 +14,7 @@
<p align="center"> <p align="center">
MiniCPM-V 2.6 <a href="https://huggingface.co/openbmb/MiniCPM-V-2_6">🤗</a> <a href="https://huggingface.co/spaces/openbmb/MiniCPM-V-2_6">🤖</a> | MiniCPM-Llama3-V 2.5 <a href="https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5/">🤗</a> <a href="https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5">🤖</a> | MiniCPM-V 2.6 <a href="https://huggingface.co/openbmb/MiniCPM-V-2_6">🤗</a> <a href="http://120.92.209.146:8887/">🤖</a> | MiniCPM-Llama3-V 2.5 <a href="https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5/">🤗</a> <a href="https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5">🤖</a> |
<a href=https://arxiv.org/abs/2408.01800>MiniCPM-Llama3-V 2.5 技术报告</a> <a href=https://arxiv.org/abs/2408.01800>MiniCPM-Llama3-V 2.5 技术报告</a>
</p> </p>
@@ -113,7 +113,7 @@
除了对个人用户友好的模型大小，MiniCPM-V 2.6 还表现出**最先进的视觉 token 密度**（即每个视觉 token 编码的像素数量）。它**仅需 640 个 token 即可处理 180 万像素图像，比大多数模型少 75%**。这一特性优化了模型的推理速度、首 token 延迟、内存占用和功耗。因此，MiniCPM-V 2.6 可以支持 iPad 等终端设备上的高效**实时视频理解**。 除了对个人用户友好的模型大小，MiniCPM-V 2.6 还表现出**最先进的视觉 token 密度**（即每个视觉 token 编码的像素数量）。它**仅需 640 个 token 即可处理 180 万像素图像，比大多数模型少 75%**。这一特性优化了模型的推理速度、首 token 延迟、内存占用和功耗。因此，MiniCPM-V 2.6 可以支持 iPad 等终端设备上的高效**实时视频理解**。
- 💫 **易于使用。** - 💫 **易于使用。**
MiniCPM-V 2.6 可以通过多种方式轻松使用：(1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpmv-main/examples/llava/README-minicpmv2.6.md) 和 [ollama](https://github.com/OpenBMB/ollama/blob/minicpm-v2.6/examples/minicpm-v2.6/README.md) 支持在本地设备上进行高效的 CPU 推理，(2) [int4](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4) 和 [GGUF](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) 格式的量化模型，有 16 种尺寸，(3) [vLLM](#vllm-部署-) 支持高吞吐量和内存高效的推理，(4) 针对新领域和任务进行微调，(5) 使用 [Gradio](#本地-webui-demo-) 快速设置本地 WebUI 演示，(6) 在线[demo](https://huggingface.co/spaces/openbmb/MiniCPM-V-2_6)即可体验。 MiniCPM-V 2.6 可以通过多种方式轻松使用：(1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpmv-main/examples/llava/README-minicpmv2.6.md) 和 [ollama](https://github.com/OpenBMB/ollama/blob/minicpm-v2.6/examples/minicpm-v2.6/README.md) 支持在本地设备上进行高效的 CPU 推理，(2) [int4](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4) 和 [GGUF](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) 格式的量化模型，有 16 种尺寸，(3) [vLLM](#vllm-部署-) 支持高吞吐量和内存高效的推理，(4) 针对新领域和任务进行微调，(5) 使用 [Gradio](#本地-webui-demo-) 快速设置本地 WebUI 演示，(6) 在线[demo](http://120.92.209.146:8887/)即可体验。
### 性能评估 <!-- omit in toc --> ### 性能评估 <!-- omit in toc -->
<div align="center"> <div align="center">
@@ -1245,7 +1245,7 @@
### Online Demo <!-- omit in toc --> ### Online Demo <!-- omit in toc -->
欢迎试用 Online Demo: [MiniCPM-V 2.6](https://huggingface.co/spaces/openbmb/MiniCPM-V-2_6) | [MiniCPM-Llama3-V 2.5](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5) | [MiniCPM-V 2.0](https://huggingface.co/spaces/openbmb/MiniCPM-V-2) 。 欢迎试用 Online Demo: [MiniCPM-V 2.6](http://120.92.209.146:8887/) | [MiniCPM-Llama3-V 2.5](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5) | [MiniCPM-V 2.0](https://huggingface.co/spaces/openbmb/MiniCPM-V-2) 。
### 本地 WebUI Demo <!-- omit in toc --> ### 本地 WebUI Demo <!-- omit in toc -->