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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>
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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.**
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### 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 -->