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<p align="center">
MiniCPM-o 2.6 <a href="https://huggingface.co/openbmb/MiniCPM-o-2_6">🤗</a> <a href="https://minicpm-omni-webdemo.modelbest.cn/"> CN🤖</a> <a href="https://minicpm-omni-webdemo-us.modelbest.cn/"> US🤖</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-o 2.6 <a href="https://huggingface.co/openbmb/MiniCPM-o-2_6">🤗</a> <a href="https://minicpm-omni-webdemo-us.modelbest.cn/"> 🤖</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> |
Technical Blog Coming Soon
</p>
@@ -131,8 +131,7 @@ Advancing popular visual capabilites from MiniCPM-V series, MiniCPM-o 2.6 can pr
In addition to its friendly size, MiniCPM-o 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-o 2.6 can efficiently support **multimodal live streaming** on end-side devices such as iPad.
- 💫 **Easy Usage.**
MiniCPM-o 2.6 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-omni/examples/llava/README-minicpmo2.6.md) support for efficient CPU inference on local devices, (2) [int4](https://huggingface.co/openbmb/MiniCPM-o-2_6-int4) and [GGUF](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) format quantized models in 16 sizes, (3) [vLLM](#efficient-inference-with-llamacpp-ollama-vllm) support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks with [LLaMA-Factory](./docs/llamafactory_train.md), (5) quick local WebUI demo setup with [Gradio](#chat-with-our-demo-on-gradio), and (6) online web demo on [CN](https://minicpm-omni-webdemo.modelbest.cn/
) server and [US](https://minicpm-omni-webdemo-us.modelbest.cn/) server.
MiniCPM-o 2.6 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-omni/examples/llava/README-minicpmo2.6.md) support for efficient CPU inference on local devices, (2) [int4](https://huggingface.co/openbmb/MiniCPM-o-2_6-int4) and [GGUF](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) format quantized models in 16 sizes, (3) [vLLM](#efficient-inference-with-llamacpp-ollama-vllm) support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks with [LLaMA-Factory](./docs/llamafactory_train.md), (5) quick local WebUI demo setup with [Gradio](#chat-with-our-demo-on-gradio), and (6) online web demo on [server](https://minicpm-omni-webdemo-us.modelbest.cn/).
**Model Architecture.**
@@ -1808,7 +1807,7 @@ We provide online and local demos powered by Hugging Face Gradio <a href='https:
### Online Demo <!-- omit in toc -->
Click here to try out the online demo of MiniCPM-o 2.6 ([CN](https://minicpm-omni-webdemo.modelbest.cn) | [US](https://minicpm-omni-webdemo-us.modelbest.cn/)) | [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).
Click here to try out the online demo of [MiniCPM-o 2.6](https://minicpm-omni-webdemo-us.modelbest.cn/) | [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 -->

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<p align="center">
MiniCPM-o 2.6 <a href="https://huggingface.co/openbmb/MiniCPM-o-2_6">🤗</a> <a href="https://minicpm-omni-webdemo.modelbest.cn/"> 国内🤖</a> <a href="https://minicpm-omni-webdemo-us.modelbest.cn/"> 国外🤖</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-o 2.6 <a href="https://huggingface.co/openbmb/MiniCPM-o-2_6">🤗</a> <a href="https://minicpm-omni-webdemo-us.modelbest.cn/"> 🤖</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> |
技术报告近期将发布
</p>
@@ -121,8 +121,7 @@ MiniCPM-o 2.6 进一步优化了 MiniCPM-V 2.6 的众多视觉理解能力，其
- 💫 **易于使用。**
MiniCPM-o 2.6 可以通过多种方式轻松使用：(1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-omni/examples/llava/README-minicpmo2.6.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](#基于-llamacppollamavllm-的高效推理) 支持高吞吐量和内存高效的推理，(4) 通过[LLaMA-Factory](./docs/llamafactory_train.md)框架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) 部署于[国内](https://minicpm-omni-webdemo.modelbest.cn/
) 或 [国外](https://minicpm-omni-webdemo-us.modelbest.cn/)服务器的在线 demo。
MiniCPM-o 2.6 可以通过多种方式轻松使用：(1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-omni/examples/llava/README-minicpmo2.6.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](#基于-llamacppollamavllm-的高效推理) 支持高吞吐量和内存高效的推理，(4) 通过[LLaMA-Factory](./docs/llamafactory_train.md)框架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) 部署于服务器的在线 [demo](https://minicpm-omni-webdemo-us.modelbest.cn/)。
**模型架构。**