Update README.md

README.md

@@ -194,7 +194,7 @@ python -m lm_eval \
     --check_integrity
 ```
 
-## Advanced Usage
+## Advanced Usage Tips
 
 For models loaded with the HuggingFace  `transformers` library, any arguments provided via `--model_args` get passed to the relevant constructor directly. This means that anything you can do with `AutoModel` can be done with our library. For example, you can pass a local path via `pretrained=` or use models finetuned with [PEFT](https://github.com/huggingface/peft) by taking the call you would run to evaluate the base model and add `,peft=PATH` to the `model_args` argument:
 ```bash
@@ -218,9 +218,9 @@ We support wildcards in task names, for example you can run all of the machine-t
 
 To save evaluation results provide an `--output_path`. We also support logging model responses with the `--log_samples` flag for post-hoc analysis.
 
-Additionally provide a directory with `--use_cache` to cache the results of prior runs. This allows you to avoid repeated execution of the same (model, task) pairs for re-scoring.
+Additionally, one can provide a directory with `--use_cache` to cache the results of prior runs. This allows you to avoid repeated execution of the same (model, task) pairs for re-scoring.
 
-For a full list supported arguments, check out the [interface](https://github.com/EleutherAI/lm-evaluation-harness/blob/big-refactor/docs/interface.md) guide!
+For a full list of supported arguments, check out the [interface](https://github.com/EleutherAI/lm-evaluation-harness/blob/big-refactor/docs/interface.md) guide in our documentation!
 
 ## How to Contribute or Learn More?