- Demonstration of running a native LLM on Android device. Now support:
- Qwen2-Instruct: 0.5B, 1.5B ...
- MiniCPM-DPO/SFT: 1B, 2.7B
- Yuan2.0-Mars-hf: 2B+
- Octopus-V2: 2.5B
- Gemma1.1-it: 2.5B
- StableLM2-Chat/Zephyr: 1.6B, 3B
- Phi2: 2.7B
- The demo models were uploaded to the drive: https://drive.google.com/drive/folders/1E43ApPcOq3I2xvb9b7aOxazTcR3hn5zK?usp=drive_link
- After downloading, place the model into the assets folder.
- Remember to decompress the *.so zip file stored in the libs/arm64-v8a folder.
- The demo models were converted from HuggingFace or ModelScope and underwent code optimizations to achieve extreme execution speed.
- Therefore, the inputs & outputs of the demo models are slightly different from the original one.
- To better adapt to ONNX Runtime on Android, the export did not use dynamic axes. Therefore, the exported ONNX model may not be optimal for x86_64 systems.
- The tokenizer.cpp and tokenizer.hpp files originated from the mnn-llm repository.
- To export the model on your own, please go to the 'Export_ONNX' folder, follow the comments to set the folder path, and then execute the ***_Export.py Python script. Next, quantize / optimize the onnx model by yourself.
- During the export process of MiniCPM-V, the Resampler always reports an error 'aten::_upsample_bilinear2d_aa' operator not supported, therefore, it is temporarily infeasible to use vision interaction.
- Updated on June 3, 2024, to use fp16 for caching past_key_values, resulting in approximately 20% faster response time compared to the previous version.
- It is not recommended to use onnxruntime.tools.convert_onnx_models_to_ort to convert to the *.ort format because it will automatically add Cast operators that changes fp16 multiplication to fp32.
- See more projects: https://dakeqq.github.io/overview/
- 在Android设备上运行本地LLM的演示。目前支持:
- 通义千问2-Instruct: 0.5B, 1.5B ...
- MiniCPM-DPO/SFT: 1B, 2.7B
- 源2.0-Mars-hf: 2B+
- Octopus-V2: 2.5B
- Gemma1.1-it: 2.5B
- StableLM2-Chat/Zephyr: 1.6B, 3B
- Phi2: 2.7B
- 演示模型已上传至云端硬盘:https://drive.google.com/drive/folders/1E43ApPcOq3I2xvb9b7aOxazTcR3hn5zK?usp=drive_link
- 百度: https://pan.baidu.com/s/1NHbUyjZ_VC-o62G13KCrSA?pwd=dake 提取码: dake
- 下载后,请将模型文件放入assets文件夹。
- 记得解压存放在libs/arm64-v8a文件夹中的*.so压缩文件。
- 演示模型是从HuggingFace或ModelScope转换来的,并经过代码优化,以实现极致执行速度。
- 因此,演示模型的输入输出与原始模型略有不同。
- 为了更好的适配ONNXRuntime-Android,导出时未使用dynamic-axes. 因此导出的ONNX模型对x86_64而言不一定是最优解.
- tokenizer.cpp和tokenizer.hpp文件源自mnn-llm仓库。
- 想自行导出模型请前往“Export_ONNX”文件夹,按照注释操作设定文件夹路径,然后执行 ***_Export.py的python脚本。下一步,自己动手量化或优化导出的ONNX模型。
- 在导出MiniCPM-V的过程中, Resampler总报错“aten::_upsample_bilinear2d_aa”算子不支持,因此暂时无法使用多模态交互。
- 2024.6月3日更新,改使用fp16缓存past_key_values,回复速度比旧版快约20%。
- 不建议使用onnxruntime.tools.convert_onnx_models_to_ort转成*.ort格式,因为它会自动添加Cast算子将fp16乘法转成fp32。
- 看更多項目: https://dakeqq.github.io/overview/
OS | Device | Backend | Model | Inference ( 1024 Context ) |
---|---|---|---|---|
Android 13 | Nubia Z50 | 8_Gen2-CPU (X3+A715) |
Qwen2-1.5B-Instruct q8f32 |
20 token/s |
Harmony 4 | P40 | Kirin_990_5G-CPU (2*A76) |
Qwen2-1.5B-Instruct q8f32 |
13 token/s |
Harmony 3 | 荣耀20S | Kirin_810-CPU (2*A76) |
Qwen2-1.5B-Instruct q8f32 |
7 token/s |
OS | Device | Backend | Model | Inference ( 1024 Context ) |
---|---|---|---|---|
Android 13 | Nubia Z50 | 8_Gen2-CPU (X3+A715) |
MiniCPM-2.7B q8f32 |
9.5 token/s |
Harmony 4 | P40 | Kirin_990_5G-CPU (2*A76) |
MiniCPM-2.7B q8f32 |
6 token/s |
Android 13 | Nubia Z50 | 8_Gen2-CPU (X3+A715) |
MiniCPM-1.3B q8f32 |
16.5 token/s |
Harmony 4 | P40 | Kirin_990_5G-CPU (2*A76) |
MiniCPM-1.3B q8f32 |
11 token/s |
OS | Device | Backend | Model | Inference ( 1024 Context ) |
---|---|---|---|---|
Android 13 | Nubia Z50 | 8_Gen2-CPU (X3+A715) |
Yuan2.0-2B-Mars-hf q8f32 |
12 token/s |
Harmony 4 | P40 | Kirin_990_5G-CPU (2*A76) |
Yuan2.0-2B-Mars-hf q8f32 |
6.5 token/s |
OS | Device | Backend | Model | Inference ( 1024 Context ) |
---|---|---|---|---|
Android 13 | Nubia Z50 | 8_Gen2-CPU (X3+A715) |
OctopusV2-2B q8f32 |
15.5 token/s |
OS | Device | Backend | Model | Inference ( 1024 Context ) |
---|---|---|---|---|
Android 13 | Nubia Z50 | 8_Gen2-CPU (X3+A715) |
Gemma1.1-it-2B q8f32 |
16 token/s |
OS | Device | Backend | Model | Inference ( 1024 Context ) |
---|---|---|---|---|
Android 13 | Nubia Z50 | 8_Gen2-CPU (X3+A715) |
StableLM2-1.6B-Chat q8f32 |
17.8 token/s |
Harmony 4 | P40 | Kirin_990_5G-CPU (2*A76) |
StableLM2-1.6B-Chat q8f32 |
11 token/s |
Harmony 3 | 荣耀20S | Kirin_810-CPU (2*A76) |
StableLM2-1.6B-Chat q8f32 |
5.5 token/s |
OS | Device | Backend | Model | Inference ( 1024 Context ) |
---|---|---|---|---|
Android 13 | Nubia Z50 | 8_Gen2-CPU (X3+A715) |
Phi2-2B-Orange-V2 q8f32 |
9.5 token/s |
Harmony 4 | P40 | Kirin_990_5G-CPU (2*A76) |
Phi2-2B-Orange-V2 q8f32 |
5.8 token/s |