6f93912c86
基于 Electron + Vue3 + TypeScript + LanceDB 的本地化 RAG 问答桌面应用。 仅支持 Linux（Kylin V10 / Ubuntu），适配 ER160（揽月 M50 NPU）和 ER25（芯动力 RPP）计算卡。 服务进程管理委托给 er_backend（HTTP 17700），前端专注 RAG 逻辑与用户交互。 包含： - src/elec_rag_ui 前端工程（Electron + Vue3） - models/ 本地嵌入模型（bge-small/base/large，ASR，标点恢复，onnx 文件由 Git LFS 管理） - docs/ 示例文档（用于测试索引与问答）
Introduction
This model is converted from https://modelscope.cn/models/iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary
The commands to generate the onnx model are given below:
pip install funasr modelscope
pip install kaldi-native-fbank torchaudio onnx onnxruntime
mkdir -p /tmp/models
cd /tmp/models
git clone https://www.modelscope.cn/iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch.git
cd punc_ct-transformer_zh-cn-common-vocab272727-pytorch
git lfs pull --include model.pt
cd /tmp
git clone https://github.com/alibaba-damo-academy/FunASR
cd FunASR/runtime/python/onnxruntime
cat >export-onnx.py <<EOF
from funasr_onnx import CT_Transformer
model_dir = "/tmp/punc_ct-transformer_zh-cn-common-vocab272727-pytorch" # model = CT_Transformer(model_dir, quantize=True) model = CT_Transformer(model_dir)
EOF
chmod +x export-onnx.py
./export-onnx.py
You will find the exported
model.onnx file inside
/tmp/models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch.
Now you can use ./add-model-metadata.py in this repo to add metadata to the generated
model.onnx.
You can use
from onnxruntime.quantization import QuantType, quantize_dynamic
quantize_dynamic(
model_input="./model.onnx",
model_output="./model.int8.onnx",
weight_type=QuantType.QUInt8,
)
to get the int8 quantized model
-rw-r--r-- 1 fangjun staff 72M Jun 18 10:33 model.int8.onnx
-rw-r--r-- 1 fangjun staff 281M Apr 12 2024 model.onnx