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Update readme, support vLLM
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iceflame89 committed Jul 19, 2024
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7 changes: 5 additions & 2 deletions README_en.md
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Expand Up @@ -29,7 +29,7 @@ Join our <a href="docs/wechat.md" target="_blank"> 💬 WeChat</a>
## News <!-- omit in toc -->

#### 📌 Pinned

* [2024.07.19] MiniCPM-Llama3-V 2.5 supports vLLM now! See [here](#vllm).
* [2024.05.28] 🚀🚀🚀 MiniCPM-Llama3-V 2.5 now fully supports its feature in llama.cpp and ollama! Please pull the latest code **of our provided forks** ([llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md), [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5/examples/minicpm-v2.5)). GGUF models in various sizes are available [here](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf/tree/main). MiniCPM-Llama3-V 2.5 series is **not supported by the official repositories yet**, and we are working hard to merge PRs. Please stay tuned!
* [2024.05.28] 💫 We now support LoRA fine-tuning for MiniCPM-Llama3-V 2.5, using only 2 V100 GPUs! See more statistics [here](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#model-fine-tuning-memory-usage-statistics).
* [2024.05.23] 🔍 We've released a comprehensive comparison between Phi-3-vision-128k-instruct and MiniCPM-Llama3-V 2.5, including benchmarks evaluations, multilingual capabilities, and inference efficiency 🌟📊🌍🚀. Click [here](./docs/compare_with_phi-3_vision.md) to view more details.
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"The Airbus A380 is a double-deck, wide-body, four-engine jet airliner made by Airbus. It is the world's largest passenger airliner and is known for its long-haul capabilities. The aircraft was developed to improve efficiency and comfort for passengers traveling over long distances. It has two full-length passenger decks, which can accommodate more passengers than a typical single-aisle airplane. The A380 has been operated by airlines such as Lufthansa, Singapore Airlines, and Emirates, among others. It is widely recognized for its unique design and significant impact on the aviation industry."
```

### Inference on Multiple GPUs
You can run MiniCPM-Llama3-V 2.5 on multiple low VRAM GPUs (12 GB or 16 GB) by distributing the model's layers across multiple GPUs. Please refer to this [tutorial](https://github.com/OpenBMB/MiniCPM-V/blob/main/docs/inference_on_multiple_gpus.md) for detailed instructions on how to load the model and inference using multiple low VRAM GPUs.


### Inference on Mac
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### Inference with vLLM<a id="vllm"></a>

<details>
<summary>Click to see how to inference MiniCPM-V 2.0 with vLLM (MiniCPM-Llama3-V 2.5 coming soon) </summary>
<summary>Click to see how to inference MiniCPM-V 2.0 and MiniCPM-Llama3-V 2.5 with vLLM </summary>
Because our pull request to vLLM is still waiting for reviewing, we fork this repository to build and test our vLLM demo. Here are the steps:

1. Clone our version of vLLM:
Expand All @@ -622,6 +624,7 @@ git clone https://github.com/OpenBMB/vllm.git
2. Install vLLM:
```shell
cd vllm
git checkout minicpmv
pip install -e .
```
3. Install timm:
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4 changes: 3 additions & 1 deletion README_zh.md
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#### 📌 置顶

* [2024.07.19] MiniCPM-Llama3-V 2.5 现已支持[vLLM](#vllm)
* [2024.05.28] 💥 MiniCPM-Llama3-V 2.5 现在在 llama.cpp 和 ollama 中完全支持其功能!**请拉取我们最新的 fork 来使用**[llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md) & [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5/examples/minicpm-v2.5)。我们还发布了各种大小的 GGUF 版本,请点击[这里](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf/tree/main)查看。请注意,**目前官方仓库尚未支持 MiniCPM-Llama3-V 2.5**,我们也正积极推进将这些功能合并到 llama.cpp & ollama 官方仓库,敬请关注!
* [2024.05.28] 💫 我们现在支持 MiniCPM-Llama3-V 2.5 的 LoRA 微调,更多内存使用统计信息可以在[这里](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#model-fine-tuning-memory-usage-statistics)找到。
* [2024.05.23] 🔍 我们添加了Phi-3-vision-128k-instruct 与 MiniCPM-Llama3-V 2.5的全面对比,包括基准测试评估、多语言能力和推理效率 🌟📊🌍🚀。点击[这里](./docs/compare_with_phi-3_vision.md)查看详细信息。
Expand Down Expand Up @@ -643,7 +644,7 @@ MiniCPM-Llama3-V 2.5 现在支持llama.cpp啦! 用法请参考我们的fork [lla

### vLLM 部署 <a id='vllm'></a>
<details>
<summary>点击查看 MiniCPM-V 2.0 利用vLLM 部署运行的方法(MiniCPM-Llama3-V 2.5 支持vLLM将在近期推出)</summary>
<summary>点击查看 MiniCPM-V 2.0 MiniCPM-Llama3-V 2.5 利用vLLM 部署运行的方法</summary>
由于我们对 vLLM 提交的 PR 还在 review 中,因此目前我们 fork 了一个 vLLM 仓库以供测试使用。

1. 首先克隆我们 fork 的 vLLM 库:
Expand All @@ -653,6 +654,7 @@ git clone https://github.com/OpenBMB/vllm.git
2. 安装 vLLM 库:
```shell
cd vllm
git checkout minicpmv
pip install -e .
```
3. 安装 timm 库:
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