Update README.md

mrdbourke · Dec 23, 2023 · 2d8e6cb · 2d8e6cb
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@@ -451,7 +451,7 @@ Notes:
 
 ### Geekbench ML
 
-All tests done using [Geekbench ML 0.6.0](https://www.geekbench.com/ml/) for Mac.
+All tests were done using [Geekbench ML 0.6.0](https://www.geekbench.com/ml/) for Mac.
 
 Tests include a series of [inference-only benchmarks](https://www.geekbench.com/doc/ml-0.6-inference-workloads.pdf) across different domains.
 
@@ -524,4 +524,4 @@ In summary my recommendations are:
 * Add total memory count + num GPU cores to results e.g. "Apple_M1_Pro_18GB_Memory_14_GPU_Cores..."
 * Add scikit-learn/XGBoost tests, e.g. 100,000 rows, 1,000,000 rows?
 * Could I use Keras 3.0 for the same code to run on multiple backends? :thinking:
-* Apple has recently released a deep learning framework called [`MLX`](https://github.com/ml-explore/mlx) which is designed for Apple Silicon, this may significantly improve speed on Apple Silicon Macs, see the `mlx/` directory for more. See this example of Llama 2 running on MLX - https://huggingface.co/mlx-llama/Llama-2-7b-chat-mlx 
+* Apple has recently released a deep learning framework called [`MLX`](https://github.com/ml-explore/mlx) which is designed for Apple Silicon, this may significantly improve speed on Apple Silicon Macs, see the `mlx/` directory for more. See this example of Llama 2 running on MLX - https://huggingface.co/mlx-llama/Llama-2-7b-chat-mlx