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Fix typos in the README #2806

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merged 2 commits into from
Nov 23, 2023
Merged

Fix typos in the README #2806

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24f2539
Fix typos in the README
bryant1410 Nov 22, 2023
cc3c9a9
Merge branch 'master' into patch-4
msaroufim Nov 23, 2023
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8 changes: 4 additions & 4 deletions README.md
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Expand Up @@ -5,7 +5,7 @@
![Benchmark Nightly](https://github.com/pytorch/serve/actions/workflows/benchmark_nightly.yml/badge.svg)
![Docker Regression Nightly](https://github.com/pytorch/serve/actions/workflows/regression_tests_docker.yml/badge.svg)

TorchServe is a flexible and easy to use tool for serving and scaling PyTorch models in production.
TorchServe is a flexible and easy-to-use tool for serving and scaling PyTorch models in production.

Requires python >= 3.8

Expand Down Expand Up @@ -67,9 +67,9 @@ Refer to [torchserve docker](docker/README.md) for details.
* [Kubeflow](https://v0-5.kubeflow.org/docs/components/pytorchserving/)
* [MLflow](https://github.com/mlflow/mlflow-torchserve)
* Export your model for optimized inference. Torchscript out of the box, [PyTorch Compiler](examples/pt2/README.md) preview, [ORT and ONNX](https://github.com/pytorch/serve/blob/master/docs/performance_guide.md), [IPEX](https://github.com/pytorch/serve/tree/master/examples/intel_extension_for_pytorch), [TensorRT](https://github.com/pytorch/serve/blob/master/docs/performance_guide.md), [FasterTransformer](https://github.com/pytorch/serve/tree/master/examples/FasterTransformer_HuggingFace_Bert), FlashAttention (Better Transformers)
* [Performance Guide](docs/performance_guide.md): builtin support to optimize, benchmark and profile PyTorch and TorchServe performance
* [Expressive handlers](CONTRIBUTING.md): An expressive handler architecture that makes it trivial to support inferencing for your usecase with [many supported out of the box](https://github.com/pytorch/serve/tree/master/ts/torch_handler)
* [Metrics API](docs/metrics.md): out of box support for system level metrics with [Prometheus exports](https://github.com/pytorch/serve/tree/master/examples/custom_metrics), custom metrics,
* [Performance Guide](docs/performance_guide.md): builtin support to optimize, benchmark, and profile PyTorch and TorchServe performance
* [Expressive handlers](CONTRIBUTING.md): An expressive handler architecture that makes it trivial to support inferencing for your use case with [many supported out of the box](https://github.com/pytorch/serve/tree/master/ts/torch_handler)
* [Metrics API](docs/metrics.md): out-of-the-box support for system-level metrics with [Prometheus exports](https://github.com/pytorch/serve/tree/master/examples/custom_metrics), custom metrics,
* [Large Model Inference Guide](docs/large_model_inference.md): With support for GenAI, LLMs including
* Fast Kernels with FlashAttention v2, continuous batching and streaming response
* PyTorch [Tensor Parallel](examples/large_models/tp_llama) preview, [Pipeline Parallel](examples/large_models/Huggingface_pippy)
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