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@@ -25,8 +25,8 @@
## News <!-- omit in toc -->
* [2024.05.24] We release the [MiniCPM-Llama3-V 2.5 gguf](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf), which supports [llama.cpp](#inference-with-llamacpp) inference and provides a 6~8 token/s smooth decoding on mobile phones. Try it now!
* [2024.05.23] 🔥🔥🔥 MiniCPM-V tops GitHub Trending and Hugging Face Trending! Our demo, retweeted by Gradio’s official account is available at [here](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5). Come and try it out!
* [2024.05.24] We release the MiniCPM-Llama3-V 2.5 [gguf](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf), which supports [llama.cpp](#inference-with-llamacpp) inference and provides a 6~8 token/s smooth decoding on mobile phones. Try it now!
* [2024.05.23] 🔥🔥🔥 MiniCPM-V tops GitHub Trending and Hugging Face Trending! Our demo, recommonded by Gradio’s official account from Hugging Face, is available at [here](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5). Come and try it out!
* [2024.05.23] 🔍 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](./docs/compare_with_phi-3_vision.md) 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 end-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.
@@ -478,13 +478,13 @@ pip install -r requirements.txt
### Model Zoo
| Model | Memory | &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp; Description | Download |
|:-----------|:-----------:|:-------------------|:---------------:|
| MiniCPM-Llama3-V 2.5 | 19 GB | The lastest version, achieving state-of-the end-side multimodal performance. | [🤗](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5/) &nbsp;&nbsp; [<img src="./assets/modelscope_logo.png" width="20px"></img>](https://modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5) |
| MiniCPM-Llama3-V 2.5 gguf | 5 GB | The gguf version, lower GPU memory and faster inference. | [🤗](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) &nbsp;&nbsp;[<img src="./assets/modelscope_logo.png" width="20px"></img>](https://modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5-gguf) |
| MiniCPM-Llama3-V 2.5 int4 | 8 GB | The int4 quantized version，lower GPU memory usage. | [🤗](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-int4/) &nbsp;&nbsp; [<img src="./assets/modelscope_logo.png" width="20px"></img>](https://modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5-int4) |
| MiniCPM-V 2.0 | 8 GB | Light version, balance the performance the computation cost. | [🤗](https://huggingface.co/openbmb/MiniCPM-V-2) &nbsp;&nbsp; [<img src="./assets/modelscope_logo.png" width="20px"></img>](https://modelscope.cn/models/OpenBMB/MiniCPM-V-2) |
| MiniCPM-V 1.0 | 7 GB | Lightest version, achieving the fastest inference. | [🤗](https://huggingface.co/openbmb/MiniCPM-V) &nbsp;&nbsp; [<img src="./assets/modelscope_logo.png" width="20px"></img>](https://modelscope.cn/models/OpenBMB/MiniCPM-V) |
| Model | Device | Memory | &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp; Description | Download |
|:-----------|:--:|:-----------:|:-------------------|:---------------:|
| MiniCPM-Llama3-V 2.5 | GPU | 19 GB | The lastest version, achieving state-of-the end-side multimodal performance. | [🤗](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5/) &nbsp;&nbsp; [<img src="./assets/modelscope_logo.png" width="20px"></img>](https://modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5) |
| MiniCPM-Llama3-V 2.5 gguf | CPU | 5 GB | The gguf version, lower GPU memory and faster inference. | [🤗](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) &nbsp;&nbsp;[<img src="./assets/modelscope_logo.png" width="20px"></img>](https://modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5-gguf) |
| MiniCPM-Llama3-V 2.5 int4 | GPU | 8 GB | The int4 quantized version，lower GPU memory usage. | [🤗](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-int4/) &nbsp;&nbsp; [<img src="./assets/modelscope_logo.png" width="20px"></img>](https://modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5-int4) |
| MiniCPM-V 2.0 | GPU | 8 GB | Light version, balance the performance the computation cost. | [🤗](https://huggingface.co/openbmb/MiniCPM-V-2) &nbsp;&nbsp; [<img src="./assets/modelscope_logo.png" width="20px"></img>](https://modelscope.cn/models/OpenBMB/MiniCPM-V-2) |
| MiniCPM-V 1.0 | GPU | 7 GB | Lightest version, achieving the fastest inference. | [🤗](https://huggingface.co/openbmb/MiniCPM-V) &nbsp;&nbsp; [<img src="./assets/modelscope_logo.png" width="20px"></img>](https://modelscope.cn/models/OpenBMB/MiniCPM-V) |
### Multi-turn Conversation
@@ -590,11 +590,7 @@ PYTORCH_ENABLE_MPS_FALLBACK=1 python web_demo_2.5.py --device mps
</details>
### Inference with llama.cpp<a id="inference-with-llamacpp"></a>
MiniCPM-Llama3-V 2.5 can run with llama.cpp now! See our fork of [llama.cpp](https://github.com/OpenBMB/llama.cpp/tree/minicpm-v2.5/examples/minicpmv) for more detail. This implementation supports smooth inference of 6~8 token/s on mobile phones<sup>1</sup>.
<small>
1. Test environment：Xiaomi 14 pro + Snapdragon 8 Gen 3
</small>
MiniCPM-Llama3-V 2.5 can run with llama.cpp now! See our fork of [llama.cpp](https://github.com/OpenBMB/llama.cpp/tree/minicpm-v2.5/examples/minicpmv) for more detail. This implementation supports smooth inference of 6~8 token/s on mobile phones (test environment：Xiaomi 14 pro + Snapdragon 8 Gen 3).
### Inference with vLLM<a id="vllm"></a>