This model is exported from
ChatGLM-6b with int8 quantization and optimized for
ONNXRuntime inference. Export code in
this repo.
Inference code with ONNXRuntime is uploaded with the model. Install requirements and run streamlit run web-ui.py to start chatting. Currently the MatMulInteger (for u8s8 data type) and DynamicQuantizeLinear operators are only supported on CPU. Arm64 with Neon support (Apple M1/M2) should be reasonably fast.
安装依赖并运行
streamlit run web-ui.py 预览模型效果。由于 ONNXRuntime 算子支持问题,目前仅能够使用 CPU 进行推理,在 Arm64 (Apple M1/M2) 上有可观的速度。具体的 ONNX 导出代码在
这个仓库中。
1git lfs clone https://huggingface.co/K024/ChatGLM-6b-onnx-u8s8
2cd ChatGLM-6b-onnx-u8s8
3pip install -r requirements.txt
4streamlit run web-ui.py
Or use
huggingface_hub python client lib to download the repo snapshot:
1from huggingface_hub import snapshot_download
2snapshot_download(repo_id="K024/ChatGLM-6b-onnx-u8s8", local_dir="./ChatGLM-6b-onnx-u8s8")
Codes are released under MIT license.
Model weights are released under the same license as ChatGLM-6b, see
MODEL LICENSE.