Update README.md (#1056)

Fixed script path

scripts/eval/README.md

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 # In-context learning (ICL) evaluation
 
-This folder contains the MosaicML LLM evaluation suite. It is a [blazingly fast](https://www.mosaicml.com/blog/llm-evaluation-for-icl), multi-GPU-enabled ICL evaluation suite with native [FSDP](https://pytorch.org/docs/stable/fsdp.html) compatibility with any model on the HuggingFace hub and any PyTorch model that implements the [`ComposerModel` interface](https://docs.mosaicml.com/projects/composer/en/latest/api_reference/generated/composer.ComposerModel.html#composermodel). We also include collection of ICL datasets we refer to as our [Eval Gauntlet](https://github.com/mosaicml/llm-foundry/blob/scripts/eval/local_data/eval_gauntlet.md) organized into 6 broad categories of competency that we expect good foundation models to have.
+This folder contains the MosaicML LLM evaluation suite. It is a [blazingly fast](https://www.mosaicml.com/blog/llm-evaluation-for-icl), multi-GPU-enabled ICL evaluation suite with native [FSDP](https://pytorch.org/docs/stable/fsdp.html) compatibility with any model on the HuggingFace hub and any PyTorch model that implements the [`ComposerModel` interface](https://docs.mosaicml.com/projects/composer/en/latest/api_reference/generated/composer.ComposerModel.html#composermodel). We also include collection of ICL datasets we refer to as our [Eval Gauntlet](./local_data/EVAL_GAUNTLET.md) organized into 6 broad categories of competency that we expect good foundation models to have.
 
 You can evaluate a model by preparing an evaluation YAML following the format of the examples in the [`scripts/eval/yamls` directory](https://github.com/mosaicml/llm-foundry/tree/main/scripts/eval/yamls).