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View All on GitHubOn-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
On-device AI across mobile, embedded and edge for PyTorch
AI Summary: The task involves creating Python bindings for a C++ API within the ExecuTorch project, specifically for its text LLM runner. This requires writing a pybind11 C++ file, integrating it into the existing CMake build system, adding the module to setup.py, and ensuring compatibility with existing .pte files and tokenizer artifacts. The goal is to allow users to run LLM models within a Python environment, such as a Jupyter notebook.
On-device AI across mobile, embedded and edge for PyTorch
AI Summary: Implement support for the `sym_max` operation within the ExecuTorch framework. This is necessary to enable successful graph lowering and model execution for models utilizing dynamic cache sizes, as highlighted by an issue encountered in the `optimum-executorch` project when exporting models from the Hugging Face Transformers library.
On-device AI across mobile, embedded and edge for PyTorch
AI Summary: Rename the CMake target `executorch` to `prim_ops_lib` in the ExecuTorch project to improve clarity and consistency with other libraries. This involves updating the CMakeLists.txt file and potentially `executorch-config.cmake` to reflect the name change and remove redundant linking code in example projects.
On-device AI across mobile, embedded and edge for PyTorch