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README.md

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 # TorchBench
 
-The TorchBench suite of [BackendBench](https://github.com/meta-pytorch/BackendBench) is designed to mimic real-world use cases. It provides operators and inputs derived from 155 model traces found in [TIMM](https://huggingface.co/timm) (67), [Hugging Face Transformers](https://huggingface.co/docs/transformers/en/index) (45), and [TorchBench](https://github.com/pytorch/benchmark) (43). (These are also the models PyTorch developers use to [validate performance](https://hud.pytorch.org/benchmark/compilers).) You can view the origin of these traces by switching the subset in the dataset viewer to `ops_traces_models`.
+The TorchBench suite of [BackendBench](https://github.com/meta-pytorch/BackendBench) is designed to mimic real-world use cases. It provides operators and inputs derived from 155 model traces found in [TIMM](https://huggingface.co/timm) (67), [Hugging Face Transformers](https://huggingface.co/docs/transformers/en/index) (45), and [TorchBench](https://github.com/pytorch/benchmark) (43). (These are also the models PyTorch developers use to [validate performance](https://hud.pytorch.org/benchmark/compilers).) You can view the origin of these traces by switching the subset in the dataset viewer to `ops_traces_models` and `torchbench` for the full dataset.
 
 When running BackendBench, much of the extra information about what you are testing is abstracted away, so you can simply run `uv run python --suite torchbench ...`. Here, however, we provide the test suite as a dataset that can be explored directly. It includes details about why certain operations and arguments were included or excluded, reflecting the careful consideration behind curating the set.