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Update docs/source/basic_tutorials/non_core_models.md

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Co-authored-by: Mishig <mishig.davaadorj@coloradocollege.edu>
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# Non-core Model Serving # Non-core Model Serving
TGI supports various LLM architectures (see full list [here](./supported_models)). If you wish to serve a model that is not one of the supported models, TGI will fallback to the `transformers` implementation of that model. This means you will be unable to use some of the features introduced by TGI, such as tensor-parallel sharding or flash attention. However, you can still get many benefits of TGI, such as continuous batching or streaming outputs. TGI supports various LLM architectures (see full list [here](../supported_models)). If you wish to serve a model that is not one of the supported models, TGI will fallback to the `transformers` implementation of that model. This means you will be unable to use some of the features introduced by TGI, such as tensor-parallel sharding or flash attention. However, you can still get many benefits of TGI, such as continuous batching or streaming outputs.
You can serve these models using Docker like below 👇 You can serve these models using Docker like below 👇