small fix

docs/parallel.md

@@ -42,8 +42,8 @@ Parallelization with multiple Python threads is possible because all computation
 ```
 
 ## Model and tensor parallelism
-Models as the [`Translator`](python/ctranslate2.Translator.rst) and [`Generator`](python/ctranslate2.Generator.rst) can be split into multiple GPUs different.
-This is very helpful when the model is too big to be load in only 1 GPU.
+Models used with [`Translator`](python/ctranslate2.Translator.rst) and [`Generator`](python/ctranslate2.Generator.rst) can be split into multiple GPUs.
+This is very useful when the model is too big to be loaded in only 1 GPU.
 
 ```python
 translator = ctranslate2.Translator(model_path, device="cuda", tensor_parallel=True)
@@ -58,33 +58,33 @@ Setup environment:
 ```
 
 Run:
-* Run the application in multiprocess to using tensor parallel:
+* Run the application in multiprocess to use tensor parallel:
 ```bash
 mpirun -np nbGPUExpected -hostfile hostfile python3 script
 ```
 
-If you're trying to run the tensor parallelism in multiple machine, there are additional configuration is needed:
+If you're trying to use tensor parallelism in multiple machines, some additional configuration is needed:
 * Make sure Master and Slave can connect to each other as a pair with ssh + pubkey
 * Export all necessary environment variables from Master to Slave like the example below:
 ```bash
 mpirun -x VIRTUAL_ENV_PROMPT -x PATH -x VIRTUAL_ENV -x _ -x LD_LIBRARY_PATH -np nbGPUExpected -hostfile hostfile python3 script
 ```
 Read more [open-mpi docs](https://www.open-mpi.org/doc/) for more information.
 
-* In this mode, the application will be run in multiprocess. We can filter out the master process by using:
+* In this mode, the application will run in multiprocess. We can filter out the master process by using:
 ```python
 if ctranslate2.MpiInfo.getCurRank() == 0:
     print(...)
 ```
 
 ```{note}
-Running model in tensor parallel mode in one machine can boost the performance but if running the model shared between multiple
-machine could be slower because of the latency in the connectivity.
+Running model in tensor parallel mode in one machine can boost the performance but if the model shared between multiple machines
+could be slower because of the latency in the connectivity.
 ```
 
 ```{note}
 In mode tensor parallel, `inter_threads` is always supported to run multiple workers. Otherwise, `device_index` no longer has any effect
-because tensor parallel mode will check only available gpus on the system and number of gpu that you want to use.
+because tensor parallel mode will check only for available gpus on the system and the number of gpus you want to use.
 ```
 
 ## Asynchronous execution